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How to use f2 method in Airtest

Best Python code snippet using Airtest

main.js

Source:main.js

1/*2âx=r sin(Ï)cos(Î¸)3ây=r sin(Ï)sin(Î¸)4âz=r cos(Ï)5*/6/* DATA SAMPLE TEMPLATE7{8  Thermo1 Object Temp,9  Thermo2 Object Temp,10  Thermo3 Object Temp,11  Thermo4 Object Temp,12  Distance,13  Pitch,14  Roll,15  Acc X,16  Acc Y,17  Acc Z,18  Thermo Ave. Device Temp,19  Time Stamp,20  Hand,21  Target,22  on/off Target Observed23}*/24var preProcess = 2; //THERMOPILE PREPROCESSING NORMALIZATION METHOD SELECTION25//sensor data object26var state = {};27    // Web Bluetooth connection -->28$( document ).ready(function() {29    button = document.getElementById("connect");30    message = document.getElementById("message");31});32//connection flag33var bluetoothDataFlag = false;34if ( 'bluetooth' in navigator === false ) {35    button.style.display = 'none';36    message.innerHTML = 'This browser doesn\'t support the <a href="https://developer.mozilla.org/en-US/docs/Web/API/Web_Bluetooth_API" target="_blank">Web Bluetooth API</a> :(';37}38const services = {39    controlService: {40        name: 'control service',41        uuid: '0000a000-0000-1000-8000-00805f9b34fb'42    }43}44const characteristics = {45    commandReadCharacteristic: {46        name: 'command read characteristic',47        uuid: '0000a001-0000-1000-8000-00805f9b34fb'48    },49    commandWriteCharacteristic: {50        name: 'command write characteristic',51        uuid: '0000a002-0000-1000-8000-00805f9b34fb'52    },53    deviceDataCharacteristic: {54        name: 'imu data characteristic',55        uuid: '0000a003-0000-1000-8000-00805f9b34fb'56    }57}58var _this;59var state = {};60var previousPose;61var sendCommandFlag = false; //global to keep track of when command is sent back to device62//let commandValue = new Uint8Array([0x01,0x03,0x02,0x03,0x01]);   //command to send back to device63let commandValue = new Uint8Array([0x99]); //command to send back to device64class ControllerWebBluetooth {65    constructor(name) {66        _this = this;67        this.name = name;68        this.services = services;69        this.characteristics = characteristics;70        this.standardServer;71    }72    connect() {73        return navigator.bluetooth.requestDevice({74            filters: [{75                        name: this.name76                    },77                    {78                        services: [services.controlService.uuid]79                    }80                ]81            })82            .then(device => {83                console.log('Device discovered', device.name);84                return device.gatt.connect();85            })86            .then(server => {87                console.log('server device: ' + Object.keys(server.device));88                this.getServices([services.controlService, ], [characteristics.commandReadCharacteristic, characteristics.commandWriteCharacteristic, characteristics.deviceDataCharacteristic], server);89            })90            .catch(error => {91                console.log('error', error)92            })93    }94    getServices(requestedServices, requestedCharacteristics, server) {95        this.standardServer = server;96        requestedServices.filter((service) => {97            if (service.uuid == services.controlService.uuid) {98                _this.getControlService(requestedServices, requestedCharacteristics, this.standardServer);99            }100        })101    }102    getControlService(requestedServices, requestedCharacteristics, server) {103        let controlService = requestedServices.filter((service) => {104            return service.uuid == services.controlService.uuid105        });106        let commandReadChar = requestedCharacteristics.filter((char) => {107            return char.uuid == characteristics.commandReadCharacteristic.uuid108        });109        let commandWriteChar = requestedCharacteristics.filter((char) => {110            return char.uuid == characteristics.commandWriteCharacteristic.uuid111        });112        // Before having access to IMU, EMG and Pose data, we need to indicate to the Myo that we want to receive this data.113        return server.getPrimaryService(controlService[0].uuid)114            .then(service => {115                console.log('getting service: ', controlService[0].name);116                return service.getCharacteristic(commandWriteChar[0].uuid);117            })118            .then(characteristic => {119                console.log('getting characteristic: ', commandWriteChar[0].name);120                // return new Buffer([0x01,3,emg_mode,imu_mode,classifier_mode]);121                // The values passed in the buffer indicate that we want to receive all data without restriction;122                //  let commandValue = new Uint8Array([0x01,0x03,0x02,0x03,0x01]);123                //this could be config info to be sent to the wearable device124                let commandValue = new Uint8Array([0x99]);125                //   characteristic.writeValue(commandValue); //disable initial write to device126            })127            .then(_ => {128                let deviceDataChar = requestedCharacteristics.filter((char) => {129                    return char.uuid == characteristics.deviceDataCharacteristic.uuid130                });131                console.log('getting service: ', controlService[0].name);132                _this.getdeviceData(controlService[0], deviceDataChar[0], server);133            })134            .catch(error => {135                console.log('error: ', error);136            })137    }138    sendControlService(requestedServices, requestedCharacteristics, server) {139        let controlService = requestedServices.filter((service) => {140            return service.uuid == services.controlService.uuid141        });142        let commandReadChar = requestedCharacteristics.filter((char) => {143            return char.uuid == characteristics.commandReadCharacteristic.uuid144        });145        let commandWriteChar = requestedCharacteristics.filter((char) => {146            return char.uuid == characteristics.commandWriteCharacteristic.uuid147        });148        // Before having access to sensor, we need to indicate to the Tingle that we want to receive this data.149        return server.getPrimaryService(controlService[0].uuid)150            .then(service => {151                console.log('getting service: ', controlService[0].name);152                return service.getCharacteristic(commandWriteChar[0].uuid);153            })154            .then(characteristic => {155                console.log('getting write command to device characteristic: ', commandWriteChar[0].name);156                // return new Buffer([0x01,3,emg_mode,imu_mode,classifier_mode]);157                // The values passed in the buffer indicate that we want to receive all data without restriction;158                let commandValue = new Uint8Array([0x99]);159                getConfig();160                commandValue[0] = targetCommand;161                console.log("CONFIG target:" + activeTarget + "  command:" + commandValue[0]);162                characteristic.writeValue(commandValue);163            })164            .then(_ => {165                //  let deviceDataChar = requestedCharacteristics.filter((char) => {return char.uuid == characteristics.deviceDataCharacteristic.uuid});166                console.log("COMMAND SENT TO DEVICE");167                sendCommandFlag = false;168                //   console.log('getting service: ', controlService[0].name);169                //  _this.getdeviceData(controlService[0], deviceDataChar[0], server);170            })171            .catch(error => {172                sendCommandFlag = false;173                console.log("COMMAND SEND ERROR");174                console.log('error: ', error);175            })176    }177    handleDeviceDataChanged(event) {178        //byteLength of deviceData DataView object is 20.179        // deviceData return {{orientation: {w: *, x: *, y: *, z: *}, accelerometer: Array, gyroscope: Array}}180        let deviceData = event.target.value;181    let accelerometerRoll   = ( (event.target.value.getUint8(0) / 255) * 360 );182    let accelerometerPitch  = ( (event.target.value.getUint8(1) / 255) * 360 );183    let proximity           = (event.target.value.getUint8(2) );184    let objectTemp1         = (event.target.value.getUint8(3) / 8) + 70;185    let objectTemp2         = (event.target.value.getUint8(4) / 8) + 70;186    let objectTemp3         = (event.target.value.getUint8(5) / 8) + 70;187    let objectTemp4         = (event.target.value.getUint8(6) / 8) + 70;188    let ambientAverage      = (event.target.value.getUint8(7) / 8) + 70;189    let batteryVoltage          = (event.target.value.getUint8(8) );190    let deviceCommand       = (event.target.value.getUint8(9) );191    let accelerometerX      = (event.target.value.getUint8(10) / 100) - 1;192    let accelerometerY      = (event.target.value.getUint8(11) / 100) - 1;193    let accelerometerZ      = (event.target.value.getUint8(12) / 100) - 1;194    let objectTemp1norm        = (event.target.value.getUint8(13) / 255 );195    let objectTemp2norm        = (event.target.value.getUint8(14) / 255 );196    let objectTemp3norm        = (event.target.value.getUint8(15) / 255 );197    let objectTemp4norm        = (event.target.value.getUint8(16) / 255 );198    let deviceNN5           = (event.target.value.getUint8(17) / 255 );199    let deviceNN7           = (event.target.value.getUint8(18) / 255 );200    console.log(accelerometerRoll + " " + accelerometerPitch + " " + proximity + " " + objectTemp1 + " " + objectTemp2 + " " + objectTemp3 + " " + objectTemp4 + " " + objectTemp1norm + " " + objectTemp2norm + " " + objectTemp3norm + " " + objectTemp4norm + " " + ambientAverage + " " + batteryVoltage);201    console.log("DEVICE NN5: " + deviceNN5 + " DEVICE NN7: " + deviceNN7);202        var data = {203            accelerometer: {204                pitch: accelerometerPitch,205                roll: accelerometerRoll,206                x: accelerometerX,207                y: accelerometerY,208                z: accelerometerZ209            },210            objectTemp: {211                a: objectTemp1,212                b: objectTemp2,213                c: objectTemp3,214                d: objectTemp4,215                e: objectTemp1norm,216                f: objectTemp2norm,217                g: objectTemp3norm,218                h: objectTemp4norm219            },220            ambientTemp: {221                a: ambientAverage222            },223            proximityData: {224                a: proximity225            },226            batteryData: {227                a: batteryVoltage228            },229            detectionData: {230                a: deviceNN5,231                b: deviceNN7232            }233        }234        state = {235            orientation: data.orientation,236            accelerometer: data.accelerometer,237            objectTemp: data.objectTemp,238            ambientTemp: data.ambientTemp,239            proximityData: data.proximityData,240            battery: data.batteryData,241            deviceDetect: data.detectionData242        }243        //move this out of state change 244        if (sendCommandFlag) {245            //this.standardServer = server;246            for (var i = 0; i < 3; i++) {247                //  sendControlService();248                _this.sendControlService([services.controlService, ], [characteristics.commandReadCharacteristic, characteristics.commandWriteCharacteristic, characteristics.deviceDataCharacteristic], _this.standardServer);249            }250            sendCommandFlag = false;251        }252        _this.onStateChangeCallback(state);253    }254    onStateChangeCallback() {}255    getdeviceData(service, characteristic, server) {256        return server.getPrimaryService(service.uuid)257            .then(newService => {258                console.log('getting characteristic: ', characteristic.name);259                return newService.getCharacteristic(characteristic.uuid)260            })261            .then(char => {262                char.startNotifications().then(res => {263                    char.addEventListener('characteristicvaluechanged', _this.handleDeviceDataChanged);264                })265            })266    }267    onStateChange(callback) {268        _this.onStateChangeCallback = callback;269    }270}271/*******************************************************************************************************************272 *********************************************** INITIALIZE *********************************************************273 ********************************************************************************************************************/274//sensor array sample data275var sensorDataArray = new Array(18).fill(0);276var masterDataArray = new Array(42);277for (var i = 0; i < masterDataArray.length; i++) {278  masterDataArray[i] = new Array;279}280var batteryVoltage;281var selectedTargetNum = 0;                   //starting target number index282var selectedTargetName = "sh-r-eyes-select"; //starting target ID283//sensor array sample data FOR CUSTOM TRAINING284var NN1TrueDataArray = new Array;285var NN1FalseDataArray = new Array;286var NN2TrueDataArray = new Array;287var NN2FalseDataArray = new Array;288var NN1Architecture = 'none';289var NN2Architecture = 'none';290var NN1NumInputs = 5;291var NN2NumInputs = 7;292//master session data array of arrays293var sensorDataSession = [];294//which samples in the session data array are part of a particular sample set295var sessionSampleSetIndex = [];296var getSamplesFlag = 0;297var getSamplesTypeFlag = 0; //0=none 1=NN1T 2=NN1F 3=NN2T 4=NN2F298//do we have a trained NN to apply to live sensor data?299var haveNNFlag1 = false;300var trainNNFlag1 = false;301var activeNNFlag1 = false;302var haveNNFlag2 = false;303var trainNNFlag2 = false;304var activeNNFlag2 = false;305//stored MM models306var loadNNFlag = false;307var loadNNTargetSelect;308var loadNNData5 = new Array(500).fill(0);309var loadNNData7 = new Array(575).fill(0);310//NN scores311var scoreArray = new Array(1).fill(0);312var scoreArraySmooth = new Array(2);313scoreArraySmooth[0] = 0.5;314scoreArraySmooth[1] = 0.5;315var pastDataNN1 = new Array(5).fill(0.5);316var pastDataNN2 = new Array(5).fill(0.5);317var initialised = false;318var timeout = null;319$(document).ready(function() {320    /*******************************************************************************************************************321     *********************************************** WEB BLUETOOTH ******************************************************322     ********************************************************************************************************************/323    //Web Bluetooth connection button and ongoing device data update function324    button.onclick = function(e) {325        var sensorController = new ControllerWebBluetooth("Tingle");326        sensorController.connect();327        //ON SENSOR DATA UPDATE328        sensorController.onStateChange(function(state) {329            bluetoothDataFlag = true;330        });331        //check for new data every X milliseconds - this is to decouple execution from Web Bluetooth actions332        setInterval(function() {333            //     bluetoothDataFlag = getBluetoothDataFlag();334            if (bluetoothDataFlag == true) {335                //      state = getState();336                /*    objectTempData = state.objectTemp;337                    proximityData = state.proximityData;338                    accelerometerData = state.accelerometer;339                    ambientTempData = state.ambientTemp;340                    heartRateData = state.heartRate; */341                timeStamp = new Date().getTime();342                //load data into global array343                sensorDataArray = new Array(18).fill(0);344                sensorDataArray[0] = state.objectTemp.a.toFixed(1);345                sensorDataArray[1] = state.objectTemp.b.toFixed(1);346                sensorDataArray[2] = state.objectTemp.c.toFixed(1);347                sensorDataArray[3] = state.objectTemp.d.toFixed(1);348                sensorDataArray[4] = state.proximityData.a.toFixed(1);349                sensorDataArray[5] = state.accelerometer.pitch.toFixed(1);350                sensorDataArray[6] = state.accelerometer.roll.toFixed(1);351                sensorDataArray[7] = state.accelerometer.x.toFixed(2);352                sensorDataArray[8] = state.accelerometer.y.toFixed(2);353                sensorDataArray[9] = state.accelerometer.z.toFixed(2);354                sensorDataArray[10] = state.ambientTemp.a.toFixed(2);355                sensorDataArray[11] = state.objectTemp.e.toFixed(4);356                sensorDataArray[12] = state.objectTemp.f.toFixed(4);357                sensorDataArray[13] = state.objectTemp.g.toFixed(4);358                sensorDataArray[14] = state.objectTemp.h.toFixed(4);359                sensorDataArray[15] = timeStamp;360                batteryVoltage = state.battery.a.toFixed(2);361                //update time series chart362                var rawThermo1Chart = ((sensorDataArray[0] - 76) / 24);363                var rawThermo2Chart = ((sensorDataArray[1] - 76) / 24);364                var rawThermo3Chart = ((sensorDataArray[2] - 76) / 24);365                var rawThermo4Chart = ((sensorDataArray[3] - 76) / 24);366                var rawPitchChart = (sensorDataArray[5] / 400);367                var rawRollChart = (sensorDataArray[6] / 400);368                var rawProximityChart = (sensorDataArray[4] / 270);369                //sensor values in bottom 2/3 of chart , 1/10 height each370                rawThermo1Chart = (rawThermo1Chart / 4.5) + 5.5 * 0.1;371                rawThermo2Chart = (rawThermo2Chart / 4.5) + 5 * 0.1;372                rawThermo3Chart = (rawThermo3Chart / 4.5) + 4.5 * 0.1;373                rawThermo4Chart = (rawThermo4Chart / 4.5) + 4 * 0.1;374                rawPitchChart = (rawPitchChart / 7) + 3 * 0.1;375                rawRollChart = (rawRollChart / 7) + 2 * 0.1;376                rawProximityChart = (rawProximityChart / 10) + 1 * 0.1;377                lineThermo1.append(timeStamp, rawThermo1Chart);378                lineThermo2.append(timeStamp, rawThermo2Chart);379                lineThermo3.append(timeStamp, rawThermo3Chart);380                lineThermo4.append(timeStamp, rawThermo4Chart);381                linePitch.append(timeStamp, rawPitchChart);382                lineRoll.append(timeStamp, rawRollChart);383                lineProximity.append(timeStamp, rawProximityChart);384                //if data sample collection has been flagged385                //  getSensorData();386                if (getSamplesFlag > 0) {387                    collectData();388                } else if (trainNNFlag1 || trainNNFlag2) {389                    //don't do anything390                } else {391                    if (haveNNFlag1 && activeNNFlag1) { //we have a NN and we want to apply to current sensor data392                        getNNScore(1);393                    } else if( loadNNFlag ){ //run loaded model394                        getNNScore(1);395                    }396                    if (haveNNFlag2 && activeNNFlag2) { //we have a NN and we want to apply to current sensor data397                        getNNScore(2);398                    } else if( loadNNFlag ){ //run loaded model399                        getNNScore(2);400                    } 401                }402                displayData();403                bluetoothDataFlag = false;404            }405        }, 200); // throttle 100 = 10Hz limit406    }407    /*******************************************************************************************************************408    **************************************** STREAMING SENSOR DATA CHART ***********************************************409    *******************************************************************************************************************/410    //add smoothie.js time series streaming data chart411    var chartHeight = 100;412    var chartWidth = $(window).width();413    $("#streaming-data-chart").html('<canvas id="chart-canvas" width="' + chartWidth + '" height="' + chartHeight + '"></canvas>');414    var streamingChart = new SmoothieChart({/*  grid: { strokeStyle:'rgb(125, 0, 0)', fillStyle:'rgb(60, 0, 0)', lineWidth: 1, millisPerLine: 250, verticalSections: 6, }, labels: { fillStyle:'rgb(60, 0, 0)' } */ });415streamingChart.streamTo(document.getElementById("chart-canvas"), 350 /*delay*/ );416    var lineThermo1 = new TimeSeries();417    var lineThermo2 = new TimeSeries();418    var lineThermo3 = new TimeSeries();419    var lineThermo4 = new TimeSeries();420    var linePitch = new TimeSeries();421    var lineRoll = new TimeSeries();422    var lineProximity = new TimeSeries();423    var lineNN1 = new TimeSeries();424    var lineNN2 = new TimeSeries();425    var lineNN1Smooth = new TimeSeries();426    var lineNN2Smooth = new TimeSeries();427    var lineNN1Var = new TimeSeries();428    var lineNN2Var = new TimeSeries();429    var lineNNAv = new TimeSeries();430    streamingChart.addTimeSeries(lineThermo1, {431        strokeStyle: 'rgb(133, 87, 35)',432        lineWidth: 3433    });434    streamingChart.addTimeSeries(lineThermo2, {435        strokeStyle: 'rgb(185, 156, 107)',436        lineWidth: 3437    });438    streamingChart.addTimeSeries(lineThermo3, {439        strokeStyle: 'rgb(143, 59, 27)',440        lineWidth: 3441    });442    streamingChart.addTimeSeries(lineThermo4, {443        strokeStyle: 'rgb(213, 117, 0)',444        lineWidth: 3445    });446    streamingChart.addTimeSeries(linePitch, {447        strokeStyle: 'rgb(128, 128, 128)',448        lineWidth: 3449    });450    streamingChart.addTimeSeries(lineRoll, {451        strokeStyle: 'rgb(240, 240, 240)',452        lineWidth: 3453    });454    streamingChart.addTimeSeries(lineProximity, {455        strokeStyle: 'rgb(128, 128, 255)',456        lineWidth: 3457    });458    streamingChart.addTimeSeries(lineNN1, {459        strokeStyle: 'rgb(72, 244, 68)',460        lineWidth: 4461    });462    streamingChart.addTimeSeries(lineNN2, {463        strokeStyle: 'rgb(244, 66, 66)',464        lineWidth: 4465    });466    streamingChart.addTimeSeries(lineNN1Smooth, {467        strokeStyle: 'rgb(58, 230, 54)',468        lineWidth: 3469    });470    streamingChart.addTimeSeries(lineNN2Smooth, {471        strokeStyle: 'rgb(230, 52, 52)',472        lineWidth: 3473    });474    streamingChart.addTimeSeries(lineNN1Var, {475        strokeStyle: 'rgb(38, 210, 32)',476        lineWidth: 3477    });478    streamingChart.addTimeSeries(lineNN2Var, {479        strokeStyle: 'rgb(210, 30, 30)',480        lineWidth: 3481    });482    streamingChart.addTimeSeries(lineNNAv, {483        strokeStyle: 'rgb(255, 0, 255)',484        lineWidth: 4485    });486    //min/max streaming chart button487    $('#circleDrop').click(function() {488        $('.card-middle').slideToggle();489        $('.close').toggleClass('closeRotate');490        var chartHeight = $(window).height() / 1.2;491        var chartWidth = $(window).width();492        if ($("#chart-size-button").hasClass('closeRotate')) {493            $("#streaming-data-chart").html('<canvas id="chart-canvas" width="' + chartWidth + '" height="' + chartHeight + '"></canvas>');494        } else {495            $("#streaming-data-chart").html('<canvas id="chart-canvas" width="' + chartWidth + '" height="' + 100 + '"></canvas>');496        }497        //hide controls498        $("#basic-interface-container, #hand-head-ui-container, #nn-slide-controls, .console, #interface-controls, #dump-print, #record-controls").toggleClass("hide-for-chart");499        //redraw chart500        streamingChart.streamTo(document.getElementById("chart-canvas"), 350 /*delay*/ );501    });502    function displayData() {503        var objectTempElement1 = document.getElementsByClassName('object-temp-1-data')[0];504        objectTempElement1.innerHTML = sensorDataArray[0];505        var objectTempElement2 = document.getElementsByClassName('object-temp-2-data')[0];506        objectTempElement2.innerHTML = sensorDataArray[1];507        var objectTempElement3 = document.getElementsByClassName('object-temp-3-data')[0];508        objectTempElement3.innerHTML = sensorDataArray[2];509        var objectTempElement4 = document.getElementsByClassName('object-temp-4-data')[0];510        objectTempElement4.innerHTML = sensorDataArray[3];511        var proximityElement = document.getElementsByClassName('proximity-data')[0];512        proximityElement.innerHTML = sensorDataArray[4];513        var accelerometerPitchDiv = document.getElementsByClassName('accelerometer-pitch-data')[0];514        accelerometerPitchDiv.innerHTML = sensorDataArray[5];515        var accelerometerRollDiv = document.getElementsByClassName('accelerometer-roll-data')[0];516        accelerometerRollDiv.innerHTML = sensorDataArray[6];517        var accelerometerXElement = document.getElementsByClassName('accelerometer-x-data')[0];518        accelerometerXElement.innerHTML = sensorDataArray[7];519        var accelerometerYElement = document.getElementsByClassName('accelerometer-y-data')[0];520        accelerometerYElement.innerHTML = sensorDataArray[8];521        var accelerometerZElement = document.getElementsByClassName('accelerometer-z-data')[0];522        accelerometerZElement.innerHTML = sensorDataArray[9];523        var ambientTempAverageElement = document.getElementsByClassName('ambient-temp-average-data')[0];524        ambientTempAverageElement.innerHTML = sensorDataArray[10];525        var batteryDataElement = document.getElementsByClassName('battery-data')[0];526        batteryDataElement.innerHTML = batteryVoltage;527    }528    function updateSampleCountDisplay() {529        $('.message-nn1-true').html(NN1TrueDataArray.length);530        $('.message-nn1-false').html(NN1FalseDataArray.length);531        $('.message-nn2-true').html(NN2TrueDataArray.length);532        $('.message-nn2-false').html(NN2FalseDataArray.length);533    }534    /**********************************************************************************************************535    ********************************** COLLECT SENSOR DATA  ***************************************************536    **********************************************************************************************************/537    function collectData() {538        var collectedDataArray = new Array(18).fill(0); //12 device 539        collectedDataArray = sensorDataArray;540        console.log("web bluetooth sensor data:");541        console.dir(collectedDataArray);542        //add sample to set543        sensorDataSession.push(collectedDataArray);544        //add to master545        masterDataArray[selectedTargetNum].push(sensorDataArray);546        $( "span[data='" + selectedTargetNum + "']" ).html(masterDataArray[selectedTargetNum].length);547        if (getSamplesTypeFlag == 1) {548            //add to master549         //   masterDataArray[selectedTargetNum].push(collectedDataArray);550         //   $( "span[data='" + selectedTargetNum + "']" ).html(masterDataArray[selectedTargetNum].length);551            NN1TrueDataArray.push(collectedDataArray);552            $('.message-nn1-true').html(NN1TrueDataArray.length);553        } else if (getSamplesTypeFlag == 2) {554            NN1FalseDataArray.push(collectedDataArray);555            $('.message-nn1-false').html(NN1FalseDataArray.length);556        } else if (getSamplesTypeFlag == 3) {557            NN2TrueDataArray.push(collectedDataArray);558            $('.message-nn2-true').html(NN2TrueDataArray.length);559        } else if (getSamplesTypeFlag == 4) {560            NN2FalseDataArray.push(collectedDataArray);561            $('.message-nn2-false').html(NN2FalseDataArray.length);562        }563        console.log("Set Index: ");564        console.dir(sessionSampleSetIndex);565        //countdown for data collection566        getSamplesFlag = getSamplesFlag - 1;567    }568    /*******************************************************************************************************************569     *********************************************** NEURAL NETWORKS ****************************************************570     ********************************************************************************************************************/571    /**572     * Attach synaptic neural net components to app object573     */574    var nnRate =        $("#rate-input").val();575    var nnIterations =  $("#iterations-input").val();576    var nnError =       $("#error-input").val();577    // ************** NEURAL NET #1578    var Neuron = synaptic.Neuron;579    var Layer = synaptic.Layer;580    var Network = synaptic.Network;581    var Trainer = synaptic.Trainer;582    var Architect = synaptic.Architect;583    var neuralNet = new Architect.LSTM(5, 5, 2, 1);584    var trainer = new Trainer(neuralNet);585    var trainingData;586    // ************* NEURAL NET #2587    var Neuron2 = synaptic.Neuron;588    var Layer2 = synaptic.Layer;589    var Network2 = synaptic.Network;590    var Trainer2 = synaptic.Trainer;591    var Architect2 = synaptic.Architect;592    var neuralNet2 = new Architect2.LSTM(7, 5, 2, 1);593    var trainer2 = new Trainer2(neuralNet2);594    var trainingData2;595    /**********************************************************************************************************596    ********************************** GET NEURAL NETWORK MODEL SCORE *****************************************597    **********************************************************************************************************/598    function getNNScore(selectNN) {599        var scoreArray = new Array(1).fill(0);600        var timeStamp = new Date().getTime();601        var displayScore;602        if (selectNN == 1) {603            var feedArray = new Array(5).fill(0);604            if(preProcess == 1){605	            feedArray[0] = sensorDataArray[0] / 101;606	            feedArray[1] = sensorDataArray[1] / 101;607	            feedArray[2] = sensorDataArray[2] / 101;608	            feedArray[3] = sensorDataArray[3] / 101; 609	        } else if(preProcess == 2) {610                feedArray[0] = (sensorDataArray[0] - 69) / (101 - 69);611                feedArray[1] = (sensorDataArray[1] - 69) / (101 - 69);612                feedArray[2] = (sensorDataArray[2] - 69) / (101 - 69);613                feedArray[3] = (sensorDataArray[3] - 69) / (101 - 69);614            } else if(preProcess == 3) {615	            feedArray[0] = sensorDataArray[11];616	            feedArray[1] = sensorDataArray[12];617	            feedArray[2] = sensorDataArray[13];618	            feedArray[3] = sensorDataArray[14]; 619	        } else if(preProcess == 4) {620	        	var thermSampAv = ( (parseFloat(sensorDataArray[0]) - 69) + (parseFloat(sensorDataArray[1]) - 69) + (parseFloat(sensorDataArray[2]) - 69) + (parseFloat(sensorDataArray[3]) - 69)) / 4;621	        	feedArray[0] = (sensorDataArray[0] - 69) / thermSampAv - 0.5;622	            feedArray[1] = (sensorDataArray[1] - 69) / thermSampAv - 0.5;623	            feedArray[2] = (sensorDataArray[2] - 69) / thermSampAv - 0.5;624	            feedArray[3] = (sensorDataArray[3] - 69) / thermSampAv - 0.5; 625	        } else if(preProcess == 5) {626                var thermSampMin = Math.min( parseFloat(sensorDataArray[0]), Math.min( parseFloat(sensorDataArray[1]), Math.min( parseFloat(sensorDataArray[2]), parseFloat(sensorDataArray[3]) ) ) );627                feedArray[0] = (sensorDataArray[0] - 69) / (thermSampMin - 69);628                feedArray[1] = (sensorDataArray[1] - 69) / (thermSampMin - 69);629                feedArray[2] = (sensorDataArray[2] - 69) / (thermSampMin - 69);630                feedArray[3] = (sensorDataArray[3] - 69) / (thermSampMin - 69);631            }632            feedArray[4] = sensorDataArray[4] / 250;633            // use trained NN or loaded NN634            if (haveNNFlag1 && activeNNFlag1) {635                scoreArray = neuralNet.activate(feedArray); //RUN TRAINED NN MODEL636            } else if (loadNNFlag) {637                scoreArray = runStoredModel5(feedArray);    //RUN STORED NN MODEL638            }639            console.log("NN1 FEED ARRAY: " + feedArray);640            console.log("NN1 SCORE ARRAY: " + scoreArray);641            displayScore = scoreArray[0].toFixed(4) * 100;642            displayScore = displayScore.toFixed(2);643            $(".message-nn1-score").html(displayScore + '%');644            var rawLineNN1Chart = scoreArray[0].toFixed(4);645            rawLineNN1Chart = (rawLineNN1Chart / 2) + 0.7;646            lineNN1.append(timeStamp, rawLineNN1Chart);647            //RUNNING AVERAGE648            scoreArraySmooth[0] = (parseFloat(scoreArraySmooth[0]) + parseFloat(scoreArray[0]) ) / 2;649            var rawLineNN1ChartSmooth = scoreArraySmooth[0].toFixed(4);650            rawLineNN1ChartSmooth = (rawLineNN1ChartSmooth / 2) + 0.7;651            lineNN1Smooth.append(timeStamp, rawLineNN1ChartSmooth);652            console.log("rawLineNN1ChartSmooth: " + rawLineNN1ChartSmooth);653            //VARIANCE654            //update historical data and calcualte average655            var NN1Av = 0;656            for(var j = 0; j < 4; j++){ 657            	pastDataNN1[j] = pastDataNN1[j + 1]; 658            	NN1Av = NN1Av + pastDataNN1[j + 1];659            }660            pastDataNN1[4] = parseFloat(scoreArray[0]);661            NN1Av = (NN1Av + pastDataNN1[4]) / 5;662            //calculate variance663            var NN1Var = 0;664	        for(var k = 0; k < 5; k++){ 665	        	NN1Var = NN1Var + Math.abs(NN1Av - pastDataNN1[k]);666	        }667	        NN1Var = NN1Var / 5;668	        lineNN1Var.append(timeStamp, NN1Var);669	        console.log("NN1 Variance: " + (NN1Var * 100));670        } else if (selectNN == 2) {671            var feedArray = new Array(7).fill(0);672            if(preProcess == 1){673	            feedArray[0] = sensorDataArray[0] / 101;674	            feedArray[1] = sensorDataArray[1] / 101;675	            feedArray[2] = sensorDataArray[2] / 101;676	            feedArray[3] = sensorDataArray[3] / 101; 677	        } else if(preProcess == 2) {678                feedArray[0] = (sensorDataArray[0] - 69) / (101 - 69);679                feedArray[1] = (sensorDataArray[1] - 69) / (101 - 69);680                feedArray[2] = (sensorDataArray[2] - 69) / (101 - 69);681                feedArray[3] = (sensorDataArray[3] - 69) / (101 - 69);682            } else if(preProcess == 3) {683	            feedArray[0] = sensorDataArray[11];684	            feedArray[1] = sensorDataArray[12];685	            feedArray[2] = sensorDataArray[13];686	            feedArray[3] = sensorDataArray[14]; 687	        } else if(preProcess == 4) {688	        	var thermSampAv = ( (parseFloat(sensorDataArray[0]) - 69) + (parseFloat(sensorDataArray[1]) - 69) + (parseFloat(sensorDataArray[2]) - 69) + (parseFloat(sensorDataArray[3]) - 69)) / 4;689	        	feedArray[0] = (sensorDataArray[0] - 69) / thermSampAv - 0.5;690	            feedArray[1] = (sensorDataArray[1] - 69) / thermSampAv - 0.5;691	            feedArray[2] = (sensorDataArray[2] - 69) / thermSampAv - 0.5;692	            feedArray[3] = (sensorDataArray[3] - 69) / thermSampAv - 0.5; 693	        } else if(preProcess == 5) {694                var thermSampMin = Math.min( parseFloat(sensorDataArray[0]), Math.min( parseFloat(sensorDataArray[1]), Math.min( parseFloat(sensorDataArray[2]), parseFloat(sensorDataArray[3]) ) ) );695                feedArray[0] = (sensorDataArray[0] - 69) / (thermSampMin - 69);696                feedArray[1] = (sensorDataArray[1] - 69) / (thermSampMin - 69);697                feedArray[2] = (sensorDataArray[2] - 69) / (thermSampMin - 69);698                feedArray[3] = (sensorDataArray[3] - 69) / (thermSampMin - 69);699            }700            feedArray[4] = sensorDataArray[4] / 250;701            feedArray[5] = sensorDataArray[5] / 360;702            feedArray[6] = sensorDataArray[6] / 360;703            if (haveNNFlag2 && activeNNFlag2) {704                scoreArray = neuralNet2.activate(feedArray);    //RUN TRAINED NN MODEL705            } else if (loadNNFlag) {706                scoreArray = runStoredModel7(feedArray);        //RUN STORED NN MODEL707            }708            console.log("NN2 FEED ARRAY: " + feedArray);709            console.log("NN2 SCORE ARRAY: " + scoreArray);710            displayScore = scoreArray[0].toFixed(4) * 100;711            displayScore = displayScore.toFixed(2);712            $(".message-nn2-score").html(displayScore + '%');713            var rawLineNN2Chart = scoreArray[0].toFixed(4);714            rawLineNN2Chart = (rawLineNN2Chart / 2) + 0.7;715            lineNN2.append(timeStamp, rawLineNN2Chart);716            //RUNNING AVERAGE717            scoreArraySmooth[1] = ( parseFloat(scoreArraySmooth[1]) + parseFloat(scoreArraySmooth[1]) + parseFloat(scoreArray[0]) ) / 3;718            var rawLineNN2ChartSmooth = scoreArraySmooth[1].toFixed(4);719            rawLineNN2ChartSmooth = (rawLineNN2ChartSmooth / 2) + 0.7;720            lineNN2Smooth.append(timeStamp, rawLineNN2ChartSmooth);721            console.log("rawLineNN2ChartSmooth: " + rawLineNN2ChartSmooth);722            //VARIANCE723            //update historical data and calcualte average724            var NN2Av = 0;725            for(var j = 0; j < 4; j++){ 726            	pastDataNN2[j] = pastDataNN2[j + 1]; 727            	NN2Av = NN2Av + pastDataNN2[j + 1];728            }729            pastDataNN2[4] = parseFloat(scoreArray[0]);730            NN2Av = (NN2Av + pastDataNN2[4]) / 5;731            //calculate variance732            var NN2Var = 0;733	        for(var k = 0; k < 5; k++){ 734	        	NN2Var = NN2Var + Math.abs(NN2Av - pastDataNN2[k]);735	        }736	        NN2Var = NN2Var / 5;737	        lineNN2Var.append(timeStamp, NN2Var);738	        console.log("NN2 Variance: " + (NN2Var * 100));739	        //PLOT AVERAGE OF NN1 and NN2740	        var rawLineNNAv = ( parseFloat(scoreArraySmooth[0]) + parseFloat(scoreArraySmooth[1]) ) / 2;741	        rawLineNNAv = (rawLineNNAv / 2) + 0.7;742	        lineNNAv.append(timeStamp, rawLineNNAv);743	    }744    }745    /**********************************************************************************************************746    ********************************** TRAIN NEURAL NETWORK MODEL *********************************************747    **********************************************************************************************************/748    function trainNN(selectNN) {749        //'5:2:1', '5:5:1', '5:5:2:1', '5:5:5:1', '7:2:1', '7:7:1', '7:7:2:1', '7:7:7:1'750        //  var processedDataSession = sensorDataSession;751        var processedDataSession = new Array;752        var falseDataArray = new Array;753        var trueDataArray = new Array;754        trainingData = new Array;755        trainingData2 = new Array;756        nnRate = $("#rate-input").val();757        nnIterations = $("#iterations-input").val();758        nnError = $("#error-input").val();759    /*    if (selectNN == 1) {760            trueDataArray = NN1TrueDataArray;761            falseDataArray = NN1FalseDataArray;762        } else if (selectNN == 2) {763            trueDataArray = NN2TrueDataArray;764            falseDataArray = NN2FalseDataArray;765        }   */766        /* cycle the position text field inputs */767        console.log("***FINDING DATA TO TRAIN AGAINST***");768        $( ".target-select-text" ).each(function() {769            var positionSelectedSampleTotal = parseInt( $( this ).val() );770            var falseSelectElementID = $(this).attr("id"); 771            console.log(falseSelectElementID + " : " + positionSelectedSampleTotal);772            if(positionSelectedSampleTotal > 0){773                var falseSelectElementIndex = parseInt( $(this).attr("num") );774           //     var falseSelectElementID = parseInt( $(this).attr("id") );775                var falseSelectLength = masterDataArray[falseSelectElementIndex].length;776                console.log("** " + positionSelectedSampleTotal + " SAMPLES FROM " + falseSelectElementID);777                console.log("false select length: " + falseSelectLength);778                //asking for more data than exists779                if(positionSelectedSampleTotal >= falseSelectLength){780                    for(var j = 0; j < falseSelectLength; j++){781                        falseDataArray.push(masterDataArray[falseSelectElementIndex][j]);782                    }783                } else {784                /*	var j=0;785                    while(j < positionSelectedSampleTotal){786                    	var oneQuartPlace = Math.round( (falseSelectLength / 4) * 1) + j - 1;  //get data from different places787                        var midPlace = Math.round( (falseSelectLength / 4) * 2) + j - 1;788                        var threeQuartPlace = Math.round( (falseSelectLength / 4) * 3) + j - 1;789                        console.log("quarter place:" + oneQuartPlace + " half place:" + midPlace + " three quarter place:" + threeQuartPlace);790                        if(j < positionSelectedSampleTotal){ falseDataArray.push(masterDataArray[falseSelectElementIndex][j]); j++;791                        792		                    if(j < positionSelectedSampleTotal && oneQuartPlace < falseSelectLength){ falseDataArray.push(masterDataArray[falseSelectElementIndex][oneQuartPlace]); j++; 793		                        794		                    	if(j < positionSelectedSampleTotal && midPlace < falseSelectLength){ falseDataArray.push(masterDataArray[falseSelectElementIndex][midPlace]); j++; 795		                    		if(j < positionSelectedSampleTotal && threeQuartPlace < falseSelectLength){ falseDataArray.push(masterDataArray[falseSelectElementIndex][threeQuartPlace]); j++; }796		                    	}797		                    }798		                }799                    } */800                    var trackRandom = new Array(2000).fill(0);801                    var countFalseSamples = 0;802                    while(countFalseSamples < positionSelectedSampleTotal){803                        //random sample in target position array of samples804                        var randomSampleIndex = Math.floor(Math.random() * falseSelectLength) + 1;805                        //make sure we haven't used this data point yet806                        if(trackRandom[(randomSampleIndex - 1)] == 0){807                            falseDataArray.push(masterDataArray[falseSelectElementIndex][(randomSampleIndex - 1)]);808                            //done with that sample, make note in tracking array809                            trackRandom[(randomSampleIndex - 1)] = 1;810                            countFalseSamples++;811                        }812                    }813                }814            }815        });816        console.log("TRAINING AGAINST " + falseDataArray.length + " SAMPLES");817        console.log("FALSE DATA: " + falseDataArray);818        //true data is currently selected target819        trueDataArray = masterDataArray[selectedTargetNum];820                //ADD DUPLICATE TRUE DATA IF MORE FALSE DATA THAN TRUE DATA821        var trueDataCounter = 0;822        while(falseDataArray.length >  trueDataArray.length){823            trueDataArray.push(masterDataArray[selectedTargetNum][trueDataCounter]);824            trueDataCounter++;825            if(trueDataCounter > (masterDataArray[selectedTargetNum].length - 1)) trueDataCounter = 0;826        }827        console.log("TRAINING FOR " + trueDataArray.length + "TARGET SAMPLES");828        //combine true and false data829        var addSample = new Array(18).fill(0);830        for (var j = 0; j < trueDataArray.length; j++) {831            addSample = trueDataArray[j];832            addSample[17] = 1; //true833            processedDataSession.push(addSample);834        }835        for (var k = 0; k < falseDataArray.length; k++) {836            addSample = falseDataArray[k];837            addSample[17] = 0; //false838            processedDataSession.push(addSample);839        }840            neuralNet = new Architect.LSTM(5, 5, 2, 1);841            trainer = new Trainer(neuralNet);842            neuralNet2 = new Architect.LSTM(7, 5, 2, 1);843            trainer2 = new Trainer2(neuralNet2);844        for (var i = 0; i < processedDataSession.length; i++) {845            var currentSample = processedDataSession[i];846            var outputArray = new Array(1).fill(0);847            outputArray[0] = currentSample[17]; //true or false848            var inputArray = new Array(5).fill(0);849	        if(preProcess == 1){850	            inputArray[0] = currentSample[0] / 101;851	            inputArray[1] = currentSample[1] / 101;852	            inputArray[2] = currentSample[2] / 101;853	            inputArray[3] = currentSample[3] / 101; 854	        } else if(preProcess == 2) {855                inputArray[0] = (currentSample[0] - 69) / (101 - 69);856                inputArray[1] = (currentSample[1] - 69) / (101 - 69);857                inputArray[2] = (currentSample[2] - 69) / (101 - 69);858                inputArray[3] = (currentSample[3] - 69) / (101 - 69);859            } else if(preProcess == 3) {860	            inputArray[0] = currentSample[11];861	            inputArray[1] = currentSample[12];862	            inputArray[2] = currentSample[13];863	            inputArray[3] = currentSample[14]; 864	        } else if(preProcess == 4) {865	        	var thermSampAv = ( (parseFloat(currentSample[0]) - 69) + (parseFloat(currentSample[1]) - 69)  + (parseFloat(currentSample[2]) - 69)  + (parseFloat(currentSample[3]) - 69) ) / 4;866	        	console.log("average for norm: " + thermSampAv);867	        	inputArray[0] = ( (currentSample[0] - 69) / thermSampAv) - 0.5;868	            inputArray[1] = ( (currentSample[1] - 69) / thermSampAv) - 0.5;869	            inputArray[2] = ( (currentSample[2] - 69) / thermSampAv) - 0.5;870	            inputArray[3] = ( (currentSample[3] - 69) / thermSampAv) - 0.5; 871	        } else if(preProcess == 5) {872                var thermSampMin = Math.min( parseFloat(currentSample[0]), Math.min( parseFloat(currentSample[1]), Math.min( parseFloat(currentSample[2]), parseFloat(currentSample[3]) ) ) );873                inputArray[0] = (currentSample[0] - 69) / (thermSampMin - 69);874                inputArray[1] = (currentSample[1] - 69) / (thermSampMin - 69);875                inputArray[2] = (currentSample[2] - 69) / (thermSampMin - 69);876                inputArray[3] = (currentSample[3] - 69) / (thermSampMin - 69);877            }878            inputArray[4] = currentSample[4] / 250;879            var inputArray2 = new Array(7).fill(0);880	        if(preProcess == 1){881	            inputArray2[0] = currentSample[0] / 101;882	            inputArray2[1] = currentSample[1] / 101;883	            inputArray2[2] = currentSample[2] / 101;884	            inputArray2[3] = currentSample[3] / 101; 885	        } else if(preProcess == 2) {886                inputArray2[0] = (currentSample[0] - 69) / (101 - 69);887                inputArray2[1] = (currentSample[1] - 69) / (101 - 69);888                inputArray2[2] = (currentSample[2] - 69) / (101 - 69);889                inputArray2[3] = (currentSample[3] - 69) / (101 - 69);890            } else if(preProcess == 3) {891	            inputArray2[0] = currentSample[11];892	            inputArray2[1] = currentSample[12];893	            inputArray2[2] = currentSample[13];894	            inputArray2[3] = currentSample[14]; 895	        } else if(preProcess == 4) {896	        	var thermSampAv = ( (parseFloat(currentSample[0]) - 69) + (parseFloat(currentSample[1]) - 69) + (parseFloat(currentSample[2]) - 69) + (parseFloat(currentSample[3]) - 69)) / 4;897	        	inputArray2[0] = ( (currentSample[0] - 69) / thermSampAv) - 0.5;898	            inputArray2[1] = ( (currentSample[1] - 69) / thermSampAv) - 0.5;899	            inputArray2[2] = ( (currentSample[2] - 69) / thermSampAv) - 0.5;900	            inputArray2[3] = ( (currentSample[3] - 69) / thermSampAv) - 0.5; 901	        } else if(preProcess == 5) {902                var thermSampMin = Math.min( parseFloat(currentSample[0]), Math.min( parseFloat(currentSample[1]), Math.min( parseFloat(currentSample[2]), parseFloat(currentSample[3]) ) ) );903                inputArray2[0] = (currentSample[0] - 69) / (thermSampMin - 69);904                inputArray2[1] = (currentSample[1] - 69) / (thermSampMin - 69);905                inputArray2[2] = (currentSample[2] - 69) / (thermSampMin - 69);906                inputArray2[3] = (currentSample[3] - 69) / (thermSampMin - 69);907            }908            inputArray2[4] = currentSample[4] / 250;909            inputArray2[5] = currentSample[5] / 360;910            inputArray2[6] = currentSample[6] / 360;911            trainingData.push({912                input: inputArray,913                output: outputArray914            });915            trainingData2.push({916                input: inputArray2,917                output: outputArray918            });919            console.log(currentSample + " TRAINING INPUT: " + inputArray + "  --> NN# " + selectNN);920            console.log(currentSample + " TRAINING OUTPUT: " + outputArray + "  --> NN# " + selectNN);921        }922            console.log("TRAINING ON selectNN1 --> interations:" + nnIterations + "  error:" + nnError + "  rate:" + nnRate);923            trainer.train(trainingData, {924                rate: nnRate,925                iterations: nnIterations,926                error: nnError,927                shuffle: true,928                log: 5,929                cost: Trainer.cost.CROSS_ENTROPY930            });931            //we have a trained NN to use932            haveNNFlag1 = true;933            trainNNFlag1 = false;934            $('#activate-btn').addClass("haveNN");935            $('#export-btn').addClass("haveNN");936            console.log("TRAINING ON selectNN2");937            trainer2.train(trainingData2, {938                rate: nnRate,939                iterations: nnIterations,940                error: nnError,941                shuffle: true,942                log: 5,943                cost: Trainer2.cost.CROSS_ENTROPY944            });945            //we have a trained NN to use946            haveNNFlag2 = true;947            trainNNFlag2 = false;948            $('#activate2-btn').addClass("haveNN");949            $('#export2-btn').addClass("haveNN");950    }951    952    /********************************************************************************************************************953    *********************************************** RUN STORED MODEL ****************************************************954    ********************************************************************************************************************/955    function runStoredModel5(feedArray){956        var output = [];957        var F1 = loadNNData5;958        F1[3] =     feedArray[0];959        F1[5] =     feedArray[1];960        F1[7] =     feedArray[2];961        F1[9] =     feedArray[3];962        F1[11] =    feedArray[4];963        F1[0] = F1[1];F1[1] = F1[2];F1[1] += F1[3] * F1[4];F1[1] += F1[5] * F1[6];F1[1] += F1[7] * F1[8];F1[1] += F1[9] * F1[10];F1[1] += F1[11] * F1[12];F1[1] += F1[13] * F1[14];F1[1] += F1[15] * F1[16];F1[1] += F1[17] * F1[18];F1[1] += F1[19] * F1[20];F1[1] += F1[21] * F1[22];F1[23] = (1 / (1 + Math.exp(-F1[1])));F1[24] = F1[23] * (1 - F1[23]);F1[25] = F1[23];F1[26] = F1[23];F1[27] = F1[23];F1[28] = F1[23];F1[29] = F1[23];964        F1[30] = F1[31];F1[31] = F1[32];F1[31] += F1[3] * F1[33];F1[31] += F1[5] * F1[34];F1[31] += F1[7] * F1[35];F1[31] += F1[9] * F1[36];F1[31] += F1[11] * F1[37];F1[31] += F1[13] * F1[38];F1[31] += F1[15] * F1[39];F1[31] += F1[17] * F1[40];F1[31] += F1[19] * F1[41];F1[31] += F1[21] * F1[42];F1[43] = (1 / (1 + Math.exp(-F1[31])));F1[44] = F1[43] * (1 - F1[43]);F1[45] = F1[43];F1[46] = F1[43];F1[47] = F1[43];F1[48] = F1[43];F1[49] = F1[43];965        F1[50] = F1[51];F1[51] = F1[52];F1[51] += F1[3] * F1[53];F1[51] += F1[5] * F1[54];F1[51] += F1[7] * F1[55];F1[51] += F1[9] * F1[56];F1[51] += F1[11] * F1[57];F1[51] += F1[13] * F1[58];F1[51] += F1[15] * F1[59];F1[51] += F1[17] * F1[60];F1[51] += F1[19] * F1[61];F1[51] += F1[21] * F1[62];F1[63] = (1 / (1 + Math.exp(-F1[51])));F1[64] = F1[63] * (1 - F1[63]);F1[65] = F1[63];F1[66] = F1[63];F1[67] = F1[63];F1[68] = F1[63];F1[69] = F1[63];966        F1[70] = F1[71];F1[71] = F1[72];F1[71] += F1[3] * F1[73];F1[71] += F1[5] * F1[74];F1[71] += F1[7] * F1[75];F1[71] += F1[9] * F1[76];F1[71] += F1[11] * F1[77];F1[71] += F1[13] * F1[78];F1[71] += F1[15] * F1[79];F1[71] += F1[17] * F1[80];F1[71] += F1[19] * F1[81];F1[71] += F1[21] * F1[82];F1[83] = (1 / (1 + Math.exp(-F1[71])));F1[84] = F1[83] * (1 - F1[83]);F1[85] = F1[83];F1[86] = F1[83];F1[87] = F1[83];F1[88] = F1[83];F1[89] = F1[83];967        F1[90] = F1[91];F1[91] = F1[92];F1[91] += F1[3] * F1[93];F1[91] += F1[5] * F1[94];F1[91] += F1[7] * F1[95];F1[91] += F1[9] * F1[96];F1[91] += F1[11] * F1[97];F1[91] += F1[13] * F1[98];F1[91] += F1[15] * F1[99];F1[91] += F1[17] * F1[100];F1[91] += F1[19] * F1[101];F1[91] += F1[21] * F1[102];F1[103] = (1 / (1 + Math.exp(-F1[91])));F1[104] = F1[103] * (1 - F1[103]);F1[105] = F1[103];F1[106] = F1[103];F1[107] = F1[103];F1[108] = F1[103];F1[109] = F1[103];968        F1[110] = F1[111];F1[111] = F1[112];F1[111] += F1[3] * F1[113];F1[111] += F1[5] * F1[114];F1[111] += F1[7] * F1[115];F1[111] += F1[9] * F1[116];F1[111] += F1[11] * F1[117];F1[111] += F1[13] * F1[118];F1[111] += F1[15] * F1[119];F1[111] += F1[17] * F1[120];F1[111] += F1[19] * F1[121];F1[111] += F1[21] * F1[122];F1[123] = (1 / (1 + Math.exp(-F1[111])));F1[124] = F1[123] * (1 - F1[123]);F1[125] = F1[123];969        F1[126] = F1[127];F1[127] = F1[128];F1[127] += F1[3] * F1[129];F1[127] += F1[5] * F1[130];F1[127] += F1[7] * F1[131];F1[127] += F1[9] * F1[132];F1[127] += F1[11] * F1[133];F1[127] += F1[13] * F1[134];F1[127] += F1[15] * F1[135];F1[127] += F1[17] * F1[136];F1[127] += F1[19] * F1[137];F1[127] += F1[21] * F1[138];F1[139] = (1 / (1 + Math.exp(-F1[127])));F1[140] = F1[139] * (1 - F1[139]);F1[141] = F1[139];970        F1[142] = F1[143];F1[143] = F1[144];F1[143] += F1[3] * F1[145];F1[143] += F1[5] * F1[146];F1[143] += F1[7] * F1[147];F1[143] += F1[9] * F1[148];F1[143] += F1[11] * F1[149];F1[143] += F1[13] * F1[150];F1[143] += F1[15] * F1[151];F1[143] += F1[17] * F1[152];F1[143] += F1[19] * F1[153];F1[143] += F1[21] * F1[154];F1[155] = (1 / (1 + Math.exp(-F1[143])));F1[156] = F1[155] * (1 - F1[155]);F1[157] = F1[155];971        F1[158] = F1[159];F1[159] = F1[160];F1[159] += F1[3] * F1[161];F1[159] += F1[5] * F1[162];F1[159] += F1[7] * F1[163];F1[159] += F1[9] * F1[164];F1[159] += F1[11] * F1[165];F1[159] += F1[13] * F1[166];F1[159] += F1[15] * F1[167];F1[159] += F1[17] * F1[168];F1[159] += F1[19] * F1[169];F1[159] += F1[21] * F1[170];F1[171] = (1 / (1 + Math.exp(-F1[159])));F1[172] = F1[171] * (1 - F1[171]);F1[173] = F1[171];972        F1[174] = F1[175];F1[175] = F1[176];F1[175] += F1[3] * F1[177];F1[175] += F1[5] * F1[178];F1[175] += F1[7] * F1[179];F1[175] += F1[9] * F1[180];F1[175] += F1[11] * F1[181];F1[175] += F1[13] * F1[182];F1[175] += F1[15] * F1[183];F1[175] += F1[17] * F1[184];F1[175] += F1[19] * F1[185];F1[175] += F1[21] * F1[186];F1[187] = (1 / (1 + Math.exp(-F1[175])));F1[188] = F1[187] * (1 - F1[187]);F1[189] = F1[187];973        F1[190] = F1[191];F1[191] = F1[125] * F1[192] * F1[191] + F1[193];F1[191] += F1[3] * F1[194] * F1[25];F1[191] += F1[5] * F1[195] * F1[26];F1[191] += F1[7] * F1[196] * F1[27];F1[191] += F1[9] * F1[197] * F1[28];F1[191] += F1[11] * F1[198] * F1[29];F1[13] = (1 / (1 + Math.exp(-F1[191])));F1[199] = F1[13] * (1 - F1[13]);974        F1[200] = F1[201];F1[201] = F1[141] * F1[202] * F1[201] + F1[203];F1[201] += F1[3] * F1[204] * F1[45];F1[201] += F1[5] * F1[205] * F1[46];F1[201] += F1[7] * F1[206] * F1[47];F1[201] += F1[9] * F1[207] * F1[48];F1[201] += F1[11] * F1[208] * F1[49];F1[15] = (1 / (1 + Math.exp(-F1[201])));F1[209] = F1[15] * (1 - F1[15]);975        F1[210] = F1[211];F1[211] = F1[157] * F1[212] * F1[211] + F1[213];F1[211] += F1[3] * F1[214] * F1[65];F1[211] += F1[5] * F1[215] * F1[66];F1[211] += F1[7] * F1[216] * F1[67];F1[211] += F1[9] * F1[217] * F1[68];F1[211] += F1[11] * F1[218] * F1[69];F1[17] = (1 / (1 + Math.exp(-F1[211])));F1[219] = F1[17] * (1 - F1[17]);976        F1[220] = F1[221];F1[221] = F1[173] * F1[222] * F1[221] + F1[223];F1[221] += F1[3] * F1[224] * F1[85];F1[221] += F1[5] * F1[225] * F1[86];F1[221] += F1[7] * F1[226] * F1[87];F1[221] += F1[9] * F1[227] * F1[88];F1[221] += F1[11] * F1[228] * F1[89];F1[19] = (1 / (1 + Math.exp(-F1[221])));F1[229] = F1[19] * (1 - F1[19]);977        F1[230] = F1[231];F1[231] = F1[189] * F1[232] * F1[231] + F1[233];F1[231] += F1[3] * F1[234] * F1[105];F1[231] += F1[5] * F1[235] * F1[106];F1[231] += F1[7] * F1[236] * F1[107];F1[231] += F1[9] * F1[237] * F1[108];F1[231] += F1[11] * F1[238] * F1[109];F1[21] = (1 / (1 + Math.exp(-F1[231])));F1[239] = F1[21] * (1 - F1[21]);978        F1[240] = F1[241];F1[241] = F1[242];F1[241] += F1[3] * F1[243];F1[241] += F1[5] * F1[244];F1[241] += F1[7] * F1[245];F1[241] += F1[9] * F1[246];F1[241] += F1[11] * F1[247];F1[241] += F1[13] * F1[248];F1[241] += F1[15] * F1[249];F1[241] += F1[17] * F1[250];F1[241] += F1[19] * F1[251];F1[241] += F1[21] * F1[252];F1[253] = (1 / (1 + Math.exp(-F1[241])));F1[254] = F1[253] * (1 - F1[253]);F1[255] = F1[253];979        F1[256] = F1[257];F1[257] = F1[258];F1[257] += F1[3] * F1[259];F1[257] += F1[5] * F1[260];F1[257] += F1[7] * F1[261];F1[257] += F1[9] * F1[262];F1[257] += F1[11] * F1[263];F1[257] += F1[13] * F1[264];F1[257] += F1[15] * F1[265];F1[257] += F1[17] * F1[266];F1[257] += F1[19] * F1[267];F1[257] += F1[21] * F1[268];F1[269] = (1 / (1 + Math.exp(-F1[257])));F1[270] = F1[269] * (1 - F1[269]);F1[271] = F1[269];980        F1[272] = F1[273];F1[273] = F1[274];F1[273] += F1[3] * F1[275];F1[273] += F1[5] * F1[276];F1[273] += F1[7] * F1[277];F1[273] += F1[9] * F1[278];F1[273] += F1[11] * F1[279];F1[273] += F1[13] * F1[280];F1[273] += F1[15] * F1[281];F1[273] += F1[17] * F1[282];F1[273] += F1[19] * F1[283];F1[273] += F1[21] * F1[284];F1[285] = (1 / (1 + Math.exp(-F1[273])));F1[286] = F1[285] * (1 - F1[285]);F1[287] = F1[285];981        F1[288] = F1[289];F1[289] = F1[290];F1[289] += F1[3] * F1[291];F1[289] += F1[5] * F1[292];F1[289] += F1[7] * F1[293];F1[289] += F1[9] * F1[294];F1[289] += F1[11] * F1[295];F1[289] += F1[13] * F1[296];F1[289] += F1[15] * F1[297];F1[289] += F1[17] * F1[298];F1[289] += F1[19] * F1[299];F1[289] += F1[21] * F1[300];F1[301] = (1 / (1 + Math.exp(-F1[289])));F1[302] = F1[301] * (1 - F1[301]);F1[303] = F1[301];982        F1[304] = F1[305];F1[305] = F1[306];F1[305] += F1[3] * F1[307];F1[305] += F1[5] * F1[308];F1[305] += F1[7] * F1[309];F1[305] += F1[9] * F1[310];F1[305] += F1[11] * F1[311];F1[305] += F1[13] * F1[312];F1[305] += F1[15] * F1[313];F1[305] += F1[17] * F1[314];F1[305] += F1[19] * F1[315];F1[305] += F1[21] * F1[316];F1[317] = (1 / (1 + Math.exp(-F1[305])));F1[318] = F1[317] * (1 - F1[317]);F1[319] = F1[317];983        F1[320] = F1[321];F1[321] = F1[322];F1[321] += F1[3] * F1[323];F1[321] += F1[5] * F1[324];F1[321] += F1[7] * F1[325];F1[321] += F1[9] * F1[326];F1[321] += F1[11] * F1[327];F1[321] += F1[13] * F1[328];F1[321] += F1[15] * F1[329];F1[321] += F1[17] * F1[330];F1[321] += F1[19] * F1[331];F1[321] += F1[21] * F1[332];F1[321] += F1[333] * F1[334];F1[321] += F1[335] * F1[336];F1[337] = (1 / (1 + Math.exp(-F1[321])));F1[338] = F1[337] * (1 - F1[337]);F1[339] = F1[337];F1[340] = F1[337];F1[341] = F1[337];F1[342] = F1[337];F1[343] = F1[337];F1[344] = F1[337];F1[345] = F1[337];F1[346] = F1[337];F1[347] = F1[337];F1[348] = F1[337];984        F1[349] = F1[350];F1[350] = F1[351];F1[350] += F1[3] * F1[352];F1[350] += F1[5] * F1[353];F1[350] += F1[7] * F1[354];F1[350] += F1[9] * F1[355];F1[350] += F1[11] * F1[356];F1[350] += F1[13] * F1[357];F1[350] += F1[15] * F1[358];F1[350] += F1[17] * F1[359];F1[350] += F1[19] * F1[360];F1[350] += F1[21] * F1[361];F1[350] += F1[333] * F1[362];F1[350] += F1[335] * F1[363];F1[364] = (1 / (1 + Math.exp(-F1[350])));F1[365] = F1[364] * (1 - F1[364]);F1[366] = F1[364];F1[367] = F1[364];F1[368] = F1[364];F1[369] = F1[364];F1[370] = F1[364];F1[371] = F1[364];F1[372] = F1[364];F1[373] = F1[364];F1[374] = F1[364];F1[375] = F1[364];985        F1[376] = F1[377];F1[377] = F1[378];F1[377] += F1[3] * F1[379];F1[377] += F1[5] * F1[380];F1[377] += F1[7] * F1[381];F1[377] += F1[9] * F1[382];F1[377] += F1[11] * F1[383];F1[377] += F1[13] * F1[384];F1[377] += F1[15] * F1[385];F1[377] += F1[17] * F1[386];F1[377] += F1[19] * F1[387];F1[377] += F1[21] * F1[388];F1[377] += F1[333] * F1[389];F1[377] += F1[335] * F1[390];F1[391] = (1 / (1 + Math.exp(-F1[377])));F1[392] = F1[391] * (1 - F1[391]);F1[393] = F1[391];986        F1[394] = F1[395];F1[395] = F1[396];F1[395] += F1[3] * F1[397];F1[395] += F1[5] * F1[398];F1[395] += F1[7] * F1[399];F1[395] += F1[9] * F1[400];F1[395] += F1[11] * F1[401];F1[395] += F1[13] * F1[402];F1[395] += F1[15] * F1[403];F1[395] += F1[17] * F1[404];F1[395] += F1[19] * F1[405];F1[395] += F1[21] * F1[406];F1[395] += F1[333] * F1[407];F1[395] += F1[335] * F1[408];F1[409] = (1 / (1 + Math.exp(-F1[395])));F1[410] = F1[409] * (1 - F1[409]);F1[411] = F1[409];987        F1[412] = F1[413];F1[413] = F1[393] * F1[414] * F1[413] + F1[415];F1[413] += F1[3] * F1[416] * F1[339];F1[413] += F1[5] * F1[417] * F1[340];F1[413] += F1[7] * F1[418] * F1[341];F1[413] += F1[9] * F1[419] * F1[342];F1[413] += F1[11] * F1[420] * F1[343];F1[413] += F1[13] * F1[421] * F1[344];F1[413] += F1[15] * F1[422] * F1[345];F1[413] += F1[17] * F1[423] * F1[346];F1[413] += F1[19] * F1[424] * F1[347];F1[413] += F1[21] * F1[425] * F1[348];F1[333] = (1 / (1 + Math.exp(-F1[413])));F1[426] = F1[333] * (1 - F1[333]);988        F1[427] = F1[428];F1[428] = F1[411] * F1[429] * F1[428] + F1[430];F1[428] += F1[3] * F1[431] * F1[366];F1[428] += F1[5] * F1[432] * F1[367];F1[428] += F1[7] * F1[433] * F1[368];F1[428] += F1[9] * F1[434] * F1[369];F1[428] += F1[11] * F1[435] * F1[370];F1[428] += F1[13] * F1[436] * F1[371];F1[428] += F1[15] * F1[437] * F1[372];F1[428] += F1[17] * F1[438] * F1[373];F1[428] += F1[19] * F1[439] * F1[374];F1[428] += F1[21] * F1[440] * F1[375];F1[335] = (1 / (1 + Math.exp(-F1[428])));F1[441] = F1[335] * (1 - F1[335]);989        F1[442] = F1[443];F1[443] = F1[444];F1[443] += F1[3] * F1[445];F1[443] += F1[5] * F1[446];F1[443] += F1[7] * F1[447];F1[443] += F1[9] * F1[448];F1[443] += F1[11] * F1[449];F1[443] += F1[13] * F1[450];F1[443] += F1[15] * F1[451];F1[443] += F1[17] * F1[452];F1[443] += F1[19] * F1[453];F1[443] += F1[21] * F1[454];F1[443] += F1[333] * F1[455];F1[443] += F1[335] * F1[456];F1[457] = (1 / (1 + Math.exp(-F1[443])));F1[458] = F1[457] * (1 - F1[457]);F1[459] = F1[457];990        F1[460] = F1[461];F1[461] = F1[462];F1[461] += F1[3] * F1[463];F1[461] += F1[5] * F1[464];F1[461] += F1[7] * F1[465];F1[461] += F1[9] * F1[466];F1[461] += F1[11] * F1[467];F1[461] += F1[13] * F1[468];F1[461] += F1[15] * F1[469];F1[461] += F1[17] * F1[470];F1[461] += F1[19] * F1[471];F1[461] += F1[21] * F1[472];F1[461] += F1[333] * F1[473];F1[461] += F1[335] * F1[474];F1[475] = (1 / (1 + Math.exp(-F1[461])));F1[476] = F1[475] * (1 - F1[475]);F1[477] = F1[475];991        F1[478] = F1[479];F1[479] = F1[480];F1[479] += F1[13] * F1[481] * F1[255];F1[479] += F1[15] * F1[482] * F1[271];F1[479] += F1[17] * F1[483] * F1[287];F1[479] += F1[19] * F1[484] * F1[303];F1[479] += F1[21] * F1[485] * F1[319];F1[479] += F1[333] * F1[486] * F1[459];F1[479] += F1[335] * F1[487] * F1[477];F1[479] += F1[3] * F1[488];F1[479] += F1[5] * F1[489];F1[479] += F1[7] * F1[490];F1[479] += F1[9] * F1[491];F1[479] += F1[11] * F1[492];F1[493] = (1 / (1 + Math.exp(-F1[479])));F1[494] = F1[493] * (1 - F1[493]);992     //   var output = [];993        output[0] = F1[493];994        return output;995    }996    function runStoredModel7(feedArray){997        var output = [];998        var F2 = loadNNData7;999        F2[3] =     feedArray[0];1000        F2[5] =     feedArray[1];1001        F2[7] =      feedArray[2];1002        F2[9] =     feedArray[3];1003        F2[11] =    feedArray[4];1004        F2[13] =    feedArray[5];1005        F2[15] =    feedArray[6];1006        F2[0] = F2[1];F2[1] = F2[2];F2[1] += F2[3] * F2[4];F2[1] += F2[5] * F2[6];F2[1] += F2[7] * F2[8];F2[1] += F2[9] * F2[10];F2[1] += F2[11] * F2[12];F2[1] += F2[13] * F2[14];F2[1] += F2[15] * F2[16];F2[1] += F2[17] * F2[18];F2[1] += F2[19] * F2[20];F2[1] += F2[21] * F2[22];F2[1] += F2[23] * F2[24];F2[1] += F2[25] * F2[26];F2[27] = (1 / (1 + Math.exp(-F2[1])));F2[28] = F2[27] * (1 - F2[27]);F2[29] = F2[27];F2[30] = F2[27];F2[31] = F2[27];F2[32] = F2[27];F2[33] = F2[27];F2[34] = F2[27];F2[35] = F2[27];1007        F2[36] = F2[37];F2[37] = F2[38];F2[37] += F2[3] * F2[39];F2[37] += F2[5] * F2[40];F2[37] += F2[7] * F2[41];F2[37] += F2[9] * F2[42];F2[37] += F2[11] * F2[43];F2[37] += F2[13] * F2[44];F2[37] += F2[15] * F2[45];F2[37] += F2[17] * F2[46];F2[37] += F2[19] * F2[47];F2[37] += F2[21] * F2[48];F2[37] += F2[23] * F2[49];F2[37] += F2[25] * F2[50];F2[51] = (1 / (1 + Math.exp(-F2[37])));F2[52] = F2[51] * (1 - F2[51]);F2[53] = F2[51];F2[54] = F2[51];F2[55] = F2[51];F2[56] = F2[51];F2[57] = F2[51];F2[58] = F2[51];F2[59] = F2[51];1008        F2[60] = F2[61];F2[61] = F2[62];F2[61] += F2[3] * F2[63];F2[61] += F2[5] * F2[64];F2[61] += F2[7] * F2[65];F2[61] += F2[9] * F2[66];F2[61] += F2[11] * F2[67];F2[61] += F2[13] * F2[68];F2[61] += F2[15] * F2[69];F2[61] += F2[17] * F2[70];F2[61] += F2[19] * F2[71];F2[61] += F2[21] * F2[72];F2[61] += F2[23] * F2[73];F2[61] += F2[25] * F2[74];F2[75] = (1 / (1 + Math.exp(-F2[61])));F2[76] = F2[75] * (1 - F2[75]);F2[77] = F2[75];F2[78] = F2[75];F2[79] = F2[75];F2[80] = F2[75];F2[81] = F2[75];F2[82] = F2[75];F2[83] = F2[75];1009        F2[84] = F2[85];F2[85] = F2[86];F2[85] += F2[3] * F2[87];F2[85] += F2[5] * F2[88];F2[85] += F2[7] * F2[89];F2[85] += F2[9] * F2[90];F2[85] += F2[11] * F2[91];F2[85] += F2[13] * F2[92];F2[85] += F2[15] * F2[93];F2[85] += F2[17] * F2[94];F2[85] += F2[19] * F2[95];F2[85] += F2[21] * F2[96];F2[85] += F2[23] * F2[97];F2[85] += F2[25] * F2[98];F2[99] = (1 / (1 + Math.exp(-F2[85])));F2[100] = F2[99] * (1 - F2[99]);F2[101] = F2[99];F2[102] = F2[99];F2[103] = F2[99];F2[104] = F2[99];F2[105] = F2[99];F2[106] = F2[99];F2[107] = F2[99];1010        F2[108] = F2[109];F2[109] = F2[110];F2[109] += F2[3] * F2[111];F2[109] += F2[5] * F2[112];F2[109] += F2[7] * F2[113];F2[109] += F2[9] * F2[114];F2[109] += F2[11] * F2[115];F2[109] += F2[13] * F2[116];F2[109] += F2[15] * F2[117];F2[109] += F2[17] * F2[118];F2[109] += F2[19] * F2[119];F2[109] += F2[21] * F2[120];F2[109] += F2[23] * F2[121];F2[109] += F2[25] * F2[122];F2[123] = (1 / (1 + Math.exp(-F2[109])));F2[124] = F2[123] * (1 - F2[123]);F2[125] = F2[123];F2[126] = F2[123];F2[127] = F2[123];F2[128] = F2[123];F2[129] = F2[123];F2[130] = F2[123];F2[131] = F2[123];1011        F2[132] = F2[133];F2[133] = F2[134];F2[133] += F2[3] * F2[135];F2[133] += F2[5] * F2[136];F2[133] += F2[7] * F2[137];F2[133] += F2[9] * F2[138];F2[133] += F2[11] * F2[139];F2[133] += F2[13] * F2[140];F2[133] += F2[15] * F2[141];F2[133] += F2[17] * F2[142];F2[133] += F2[19] * F2[143];F2[133] += F2[21] * F2[144];F2[133] += F2[23] * F2[145];F2[133] += F2[25] * F2[146];F2[147] = (1 / (1 + Math.exp(-F2[133])));F2[148] = F2[147] * (1 - F2[147]);F2[149] = F2[147];1012        F2[150] = F2[151];F2[151] = F2[152];F2[151] += F2[3] * F2[153];F2[151] += F2[5] * F2[154];F2[151] += F2[7] * F2[155];F2[151] += F2[9] * F2[156];F2[151] += F2[11] * F2[157];F2[151] += F2[13] * F2[158];F2[151] += F2[15] * F2[159];F2[151] += F2[17] * F2[160];F2[151] += F2[19] * F2[161];F2[151] += F2[21] * F2[162];F2[151] += F2[23] * F2[163];F2[151] += F2[25] * F2[164];F2[165] = (1 / (1 + Math.exp(-F2[151])));F2[166] = F2[165] * (1 - F2[165]);F2[167] = F2[165];1013        F2[168] = F2[169];F2[169] = F2[170];F2[169] += F2[3] * F2[171];F2[169] += F2[5] * F2[172];F2[169] += F2[7] * F2[173];F2[169] += F2[9] * F2[174];F2[169] += F2[11] * F2[175];F2[169] += F2[13] * F2[176];F2[169] += F2[15] * F2[177];F2[169] += F2[17] * F2[178];F2[169] += F2[19] * F2[179];F2[169] += F2[21] * F2[180];F2[169] += F2[23] * F2[181];F2[169] += F2[25] * F2[182];F2[183] = (1 / (1 + Math.exp(-F2[169])));F2[184] = F2[183] * (1 - F2[183]);F2[185] = F2[183];1014        F2[186] = F2[187];F2[187] = F2[188];F2[187] += F2[3] * F2[189];F2[187] += F2[5] * F2[190];F2[187] += F2[7] * F2[191];F2[187] += F2[9] * F2[192];F2[187] += F2[11] * F2[193];F2[187] += F2[13] * F2[194];F2[187] += F2[15] * F2[195];F2[187] += F2[17] * F2[196];F2[187] += F2[19] * F2[197];F2[187] += F2[21] * F2[198];F2[187] += F2[23] * F2[199];F2[187] += F2[25] * F2[200];F2[201] = (1 / (1 + Math.exp(-F2[187])));F2[202] = F2[201] * (1 - F2[201]);F2[203] = F2[201];1015        F2[204] = F2[205];F2[205] = F2[206];F2[205] += F2[3] * F2[207];F2[205] += F2[5] * F2[208];F2[205] += F2[7] * F2[209];F2[205] += F2[9] * F2[210];F2[205] += F2[11] * F2[211];F2[205] += F2[13] * F2[212];F2[205] += F2[15] * F2[213];F2[205] += F2[17] * F2[214];F2[205] += F2[19] * F2[215];F2[205] += F2[21] * F2[216];F2[205] += F2[23] * F2[217];F2[205] += F2[25] * F2[218];F2[219] = (1 / (1 + Math.exp(-F2[205])));F2[220] = F2[219] * (1 - F2[219]);F2[221] = F2[219];1016        F2[222] = F2[223];F2[223] = F2[149] * F2[224] * F2[223] + F2[225];F2[223] += F2[3] * F2[226] * F2[29];F2[223] += F2[5] * F2[227] * F2[30];F2[223] += F2[7] * F2[228] * F2[31];F2[223] += F2[9] * F2[229] * F2[32];F2[223] += F2[11] * F2[230] * F2[33];F2[223] += F2[13] * F2[231] * F2[34];F2[223] += F2[15] * F2[232] * F2[35];F2[17] = (1 / (1 + Math.exp(-F2[223])));F2[233] = F2[17] * (1 - F2[17]);1017        F2[234] = F2[235];F2[235] = F2[167] * F2[236] * F2[235] + F2[237];F2[235] += F2[3] * F2[238] * F2[53];F2[235] += F2[5] * F2[239] * F2[54];F2[235] += F2[7] * F2[240] * F2[55];F2[235] += F2[9] * F2[241] * F2[56];F2[235] += F2[11] * F2[242] * F2[57];F2[235] += F2[13] * F2[243] * F2[58];F2[235] += F2[15] * F2[244] * F2[59];F2[19] = (1 / (1 + Math.exp(-F2[235])));F2[245] = F2[19] * (1 - F2[19]);1018        F2[246] = F2[247];F2[247] = F2[185] * F2[248] * F2[247] + F2[249];F2[247] += F2[3] * F2[250] * F2[77];F2[247] += F2[5] * F2[251] * F2[78];F2[247] += F2[7] * F2[252] * F2[79];F2[247] += F2[9] * F2[253] * F2[80];F2[247] += F2[11] * F2[254] * F2[81];F2[247] += F2[13] * F2[255] * F2[82];F2[247] += F2[15] * F2[256] * F2[83];F2[21] = (1 / (1 + Math.exp(-F2[247])));F2[257] = F2[21] * (1 - F2[21]);1019        F2[258] = F2[259];F2[259] = F2[203] * F2[260] * F2[259] + F2[261];F2[259] += F2[3] * F2[262] * F2[101];F2[259] += F2[5] * F2[263] * F2[102];F2[259] += F2[7] * F2[264] * F2[103];F2[259] += F2[9] * F2[265] * F2[104];F2[259] += F2[11] * F2[266] * F2[105];F2[259] += F2[13] * F2[267] * F2[106];F2[259] += F2[15] * F2[268] * F2[107];F2[23] = (1 / (1 + Math.exp(-F2[259])));F2[269] = F2[23] * (1 - F2[23]);1020        F2[270] = F2[271];F2[271] = F2[221] * F2[272] * F2[271] + F2[273];F2[271] += F2[3] * F2[274] * F2[125];F2[271] += F2[5] * F2[275] * F2[126];F2[271] += F2[7] * F2[276] * F2[127];F2[271] += F2[9] * F2[277] * F2[128];F2[271] += F2[11] * F2[278] * F2[129];F2[271] += F2[13] * F2[279] * F2[130];F2[271] += F2[15] * F2[280] * F2[131];F2[25] = (1 / (1 + Math.exp(-F2[271])));F2[281] = F2[25] * (1 - F2[25]);1021        F2[282] = F2[283];F2[283] = F2[284];F2[283] += F2[3] * F2[285];F2[283] += F2[5] * F2[286];F2[283] += F2[7] * F2[287];F2[283] += F2[9] * F2[288];F2[283] += F2[11] * F2[289];F2[283] += F2[13] * F2[290];F2[283] += F2[15] * F2[291];F2[283] += F2[17] * F2[292];F2[283] += F2[19] * F2[293];F2[283] += F2[21] * F2[294];F2[283] += F2[23] * F2[295];F2[283] += F2[25] * F2[296];F2[297] = (1 / (1 + Math.exp(-F2[283])));F2[298] = F2[297] * (1 - F2[297]);F2[299] = F2[297];1022        F2[300] = F2[301];F2[301] = F2[302];F2[301] += F2[3] * F2[303];F2[301] += F2[5] * F2[304];F2[301] += F2[7] * F2[305];F2[301] += F2[9] * F2[306];F2[301] += F2[11] * F2[307];F2[301] += F2[13] * F2[308];F2[301] += F2[15] * F2[309];F2[301] += F2[17] * F2[310];F2[301] += F2[19] * F2[311];F2[301] += F2[21] * F2[312];F2[301] += F2[23] * F2[313];F2[301] += F2[25] * F2[314];F2[315] = (1 / (1 + Math.exp(-F2[301])));F2[316] = F2[315] * (1 - F2[315]);F2[317] = F2[315];1023        F2[318] = F2[319];F2[319] = F2[320];F2[319] += F2[3] * F2[321];F2[319] += F2[5] * F2[322];F2[319] += F2[7] * F2[323];F2[319] += F2[9] * F2[324];F2[319] += F2[11] * F2[325];F2[319] += F2[13] * F2[326];F2[319] += F2[15] * F2[327];F2[319] += F2[17] * F2[328];F2[319] += F2[19] * F2[329];F2[319] += F2[21] * F2[330];F2[319] += F2[23] * F2[331];F2[319] += F2[25] * F2[332];F2[333] = (1 / (1 + Math.exp(-F2[319])));F2[334] = F2[333] * (1 - F2[333]);F2[335] = F2[333];1024        F2[336] = F2[337];F2[337] = F2[338];F2[337] += F2[3] * F2[339];F2[337] += F2[5] * F2[340];F2[337] += F2[7] * F2[341];F2[337] += F2[9] * F2[342];F2[337] += F2[11] * F2[343];F2[337] += F2[13] * F2[344];F2[337] += F2[15] * F2[345];F2[337] += F2[17] * F2[346];F2[337] += F2[19] * F2[347];F2[337] += F2[21] * F2[348];F2[337] += F2[23] * F2[349];F2[337] += F2[25] * F2[350];F2[351] = (1 / (1 + Math.exp(-F2[337])));F2[352] = F2[351] * (1 - F2[351]);F2[353] = F2[351];1025        F2[354] = F2[355];F2[355] = F2[356];F2[355] += F2[3] * F2[357];F2[355] += F2[5] * F2[358];F2[355] += F2[7] * F2[359];F2[355] += F2[9] * F2[360];F2[355] += F2[11] * F2[361];F2[355] += F2[13] * F2[362];F2[355] += F2[15] * F2[363];F2[355] += F2[17] * F2[364];F2[355] += F2[19] * F2[365];F2[355] += F2[21] * F2[366];F2[355] += F2[23] * F2[367];F2[355] += F2[25] * F2[368];F2[369] = (1 / (1 + Math.exp(-F2[355])));F2[370] = F2[369] * (1 - F2[369]);F2[371] = F2[369];1026        F2[372] = F2[373];F2[373] = F2[374];F2[373] += F2[3] * F2[375];F2[373] += F2[5] * F2[376];F2[373] += F2[7] * F2[377];F2[373] += F2[9] * F2[378];F2[373] += F2[11] * F2[379];F2[373] += F2[13] * F2[380];F2[373] += F2[15] * F2[381];F2[373] += F2[17] * F2[382];F2[373] += F2[19] * F2[383];F2[373] += F2[21] * F2[384];F2[373] += F2[23] * F2[385];F2[373] += F2[25] * F2[386];F2[373] += F2[387] * F2[388];F2[373] += F2[389] * F2[390];F2[391] = (1 / (1 + Math.exp(-F2[373])));F2[392] = F2[391] * (1 - F2[391]);F2[393] = F2[391];F2[394] = F2[391];F2[395] = F2[391];F2[396] = F2[391];F2[397] = F2[391];F2[398] = F2[391];F2[399] = F2[391];F2[400] = F2[391];F2[401] = F2[391];F2[402] = F2[391];F2[403] = F2[391];F2[404] = F2[391];1027        F2[405] = F2[406];F2[406] = F2[407];F2[406] += F2[3] * F2[408];F2[406] += F2[5] * F2[409];F2[406] += F2[7] * F2[410];F2[406] += F2[9] * F2[411];F2[406] += F2[11] * F2[412];F2[406] += F2[13] * F2[413];F2[406] += F2[15] * F2[414];F2[406] += F2[17] * F2[415];F2[406] += F2[19] * F2[416];F2[406] += F2[21] * F2[417];F2[406] += F2[23] * F2[418];F2[406] += F2[25] * F2[419];F2[406] += F2[387] * F2[420];F2[406] += F2[389] * F2[421];F2[422] = (1 / (1 + Math.exp(-F2[406])));F2[423] = F2[422] * (1 - F2[422]);F2[424] = F2[422];F2[425] = F2[422];F2[426] = F2[422];F2[427] = F2[422];F2[428] = F2[422];F2[429] = F2[422];F2[430] = F2[422];F2[431] = F2[422];F2[432] = F2[422];F2[433] = F2[422];F2[434] = F2[422];F2[435] = F2[422];1028        F2[436] = F2[437];F2[437] = F2[438];F2[437] += F2[3] * F2[439];F2[437] += F2[5] * F2[440];F2[437] += F2[7] * F2[441];F2[437] += F2[9] * F2[442];F2[437] += F2[11] * F2[443];F2[437] += F2[13] * F2[444];F2[437] += F2[15] * F2[445];F2[437] += F2[17] * F2[446];F2[437] += F2[19] * F2[447];F2[437] += F2[21] * F2[448];F2[437] += F2[23] * F2[449];F2[437] += F2[25] * F2[450];F2[437] += F2[387] * F2[451];F2[437] += F2[389] * F2[452];F2[453] = (1 / (1 + Math.exp(-F2[437])));F2[454] = F2[453] * (1 - F2[453]);F2[455] = F2[453];1029        F2[456] = F2[457];F2[457] = F2[458];F2[457] += F2[3] * F2[459];F2[457] += F2[5] * F2[460];F2[457] += F2[7] * F2[461];F2[457] += F2[9] * F2[462];F2[457] += F2[11] * F2[463];F2[457] += F2[13] * F2[464];F2[457] += F2[15] * F2[465];F2[457] += F2[17] * F2[466];F2[457] += F2[19] * F2[467];F2[457] += F2[21] * F2[468];F2[457] += F2[23] * F2[469];F2[457] += F2[25] * F2[470];F2[457] += F2[387] * F2[471];F2[457] += F2[389] * F2[472];F2[473] = (1 / (1 + Math.exp(-F2[457])));F2[474] = F2[473] * (1 - F2[473]);F2[475] = F2[473];1030        F2[476] = F2[477];F2[477] = F2[455] * F2[478] * F2[477] + F2[479];F2[477] += F2[3] * F2[480] * F2[393];F2[477] += F2[5] * F2[481] * F2[394];F2[477] += F2[7] * F2[482] * F2[395];F2[477] += F2[9] * F2[483] * F2[396];F2[477] += F2[11] * F2[484] * F2[397];F2[477] += F2[13] * F2[485] * F2[398];F2[477] += F2[15] * F2[486] * F2[399];F2[477] += F2[17] * F2[487] * F2[400];F2[477] += F2[19] * F2[488] * F2[401];F2[477] += F2[21] * F2[489] * F2[402];F2[477] += F2[23] * F2[490] * F2[403];F2[477] += F2[25] * F2[491] * F2[404];F2[387] = (1 / (1 + Math.exp(-F2[477])));F2[492] = F2[387] * (1 - F2[387]);1031        F2[493] = F2[494];F2[494] = F2[475] * F2[495] * F2[494] + F2[496];F2[494] += F2[3] * F2[497] * F2[424];F2[494] += F2[5] * F2[498] * F2[425];F2[494] += F2[7] * F2[499] * F2[426];F2[494] += F2[9] * F2[500] * F2[427];F2[494] += F2[11] * F2[501] * F2[428];F2[494] += F2[13] * F2[502] * F2[429];F2[494] += F2[15] * F2[503] * F2[430];F2[494] += F2[17] * F2[504] * F2[431];F2[494] += F2[19] * F2[505] * F2[432];F2[494] += F2[21] * F2[506] * F2[433];F2[494] += F2[23] * F2[507] * F2[434];F2[494] += F2[25] * F2[508] * F2[435];F2[389] = (1 / (1 + Math.exp(-F2[494])));F2[509] = F2[389] * (1 - F2[389]);1032        F2[510] = F2[511];F2[511] = F2[512];F2[511] += F2[3] * F2[513];F2[511] += F2[5] * F2[514];F2[511] += F2[7] * F2[515];F2[511] += F2[9] * F2[516];F2[511] += F2[11] * F2[517];F2[511] += F2[13] * F2[518];F2[511] += F2[15] * F2[519];F2[511] += F2[17] * F2[520];F2[511] += F2[19] * F2[521];F2[511] += F2[21] * F2[522];F2[511] += F2[23] * F2[523];F2[511] += F2[25] * F2[524];F2[511] += F2[387] * F2[525];F2[511] += F2[389] * F2[526];F2[527] = (1 / (1 + Math.exp(-F2[511])));F2[528] = F2[527] * (1 - F2[527]);F2[529] = F2[527];1033        F2[530] = F2[531];F2[531] = F2[532];F2[531] += F2[3] * F2[533];F2[531] += F2[5] * F2[534];F2[531] += F2[7] * F2[535];F2[531] += F2[9] * F2[536];F2[531] += F2[11] * F2[537];F2[531] += F2[13] * F2[538];F2[531] += F2[15] * F2[539];F2[531] += F2[17] * F2[540];F2[531] += F2[19] * F2[541];F2[531] += F2[21] * F2[542];F2[531] += F2[23] * F2[543];F2[531] += F2[25] * F2[544];F2[531] += F2[387] * F2[545];F2[531] += F2[389] * F2[546];F2[547] = (1 / (1 + Math.exp(-F2[531])));F2[548] = F2[547] * (1 - F2[547]);F2[549] = F2[547];1034        F2[550] = F2[551];F2[551] = F2[552];F2[551] += F2[17] * F2[553] * F2[299];F2[551] += F2[19] * F2[554] * F2[317];F2[551] += F2[21] * F2[555] * F2[335];F2[551] += F2[23] * F2[556] * F2[353];F2[551] += F2[25] * F2[557] * F2[371];F2[551] += F2[387] * F2[558] * F2[529];F2[551] += F2[389] * F2[559] * F2[549];F2[551] += F2[3] * F2[560];F2[551] += F2[5] * F2[561];F2[551] += F2[7] * F2[562];F2[551] += F2[9] * F2[563];F2[551] += F2[11] * F2[564];F2[551] += F2[13] * F2[565];F2[551] += F2[15] * F2[566];F2[567] = (1 / (1 + Math.exp(-F2[551])));F2[568] = F2[567] * (1 - F2[567]);1035      //  var output = [];1036        output[0] = F2[567];1037        return output;1038    }1039    /********************************************************************************************************************1040    *********************************************** CHANGE CURRENT TARGET ***********************************************1041    ********************************************************************************************************************/1042    $('.target-select-radio').click(function() {1043        selectedTargetNum = parseInt( $(this).attr("num") );1044        selectedTargetName = $(this).attr("id");1045        var isChecked = $('#rdSelect').prop('checked');1046        console.log("SELECTED TARGET: " + selectedTargetName + " " + selectedTargetNum + " " + isChecked);1047    });1048    /********************************************************************************************************************1049    *********************************************** SLIDER UI ***********************************************************1050    ********************************************************************************************************************/1051    var rangeSlider = function(){1052        var slider = $('.range-slider'),1053            range = $('.range-slider__range'),1054            value = $('.range-slider__value');1055          1056        slider.each(function(){1057        value.each(function(){1058            var value = $(this).prev().attr('value');1059            $(this).html(value);1060        });1061        if( $(this).hasClass('nn-architecture') ){ $('.range-slider__value.nn-architecture').html('2:5:1'); }1062        range.on('input', function(){1063            var labels = ['2:1', '2:5:1', '2:5:5:1', '3:1', '3:5:1', '3:5:5:1', '5:1', '5:5:1', '5:7:7:1'];1064            $(this).next(value).html(this.value);1065            if( $(this).hasClass('nn-architecture') ){ $(this).next(value).html( labels[this.value] ); }1066          1067          });1068        });1069    }1070    rangeSlider();1071    //RANGE SLIDER EVENT HANDLER1072    $( ".range-slider" ).each(function() {1073        if($(this).hasClass("nn-architecture")){1074            // Add labels to slider whose values 1075            // are specified by min, max and whose1076            // step is set to 11077            1078            // Get the options for this slider1079            //var opt = $(this).data().uiSlider.options;1080            // Get the number of possible values1081            var $input = $(this).find("input");1082            var min = parseInt($input.attr("min"));1083            var max = parseInt($input.attr("max"));1084            var step = parseInt($input.attr("step"));1085            var increment = parseInt($input.attr("increment"));1086            var vals = max - min; //opt.max - opt.min;1087            //if(min < 0){ vals = max + min; }1088            var labels = ['2:1', '2:5:1', '2:5:5:1', '3:1', '3:5:1', '3:5:5:1', '5:1', '5:5:1', '5:7:7:1'];1089            1090            // Space out values1091            for (var i = 0; (i * increment) <= vals; i++) {1092                var s = min + (i * increment);1093                var el = $('<label>'+ labels[s] +'</label>').css('left',( 4 + Math.abs((s-min)/vals) *($input.width() -24)+'px'));1094                //   var el = $('<label>'+ s +'</label>').css('left',( 3 + ((s-min)/vals) *($input.width() -24)+'px'));1095                if(s == 0){ el = $('<label>'+ labels[s] +'</label>').css('left',( 21 + Math.abs((s-min)/vals) *($input.width() -24)+'px')); }1096                if(s == vals){ el = $('<label>'+ labels[s] +'</label>').css('left',( -20 + Math.abs((s-min)/vals) *($input.width() -24)+'px')); }1097                $(this).append(el);1098            }1099        }  1100    });1101    /*******************************************************************************************************************1102     ******************************************* NEURAL NETWORK BUTTONS *************************************************1103     ********************************************************************************************************************/1104    $('#train-btn').click(function() {1105        console.log("train button 1");1106        trainNNFlag1 = true;1107        trainNN(1);1108    });1109    $('#activate-btn').click(function() {1110        console.log("activate button");1111        activeNNFlag1 = true;1112        $('#activate-btn').toggleClass("activatedNN");1113        //if loaded NN, turn off1114        if (loadNNFlag) {1115            loadNNFlag = false;1116            $('#load-nn-btn').toggleClass("activatedNN");1117        }1118    });1119    $('#train2-btn').click(function() {1120        console.log("train button 2");1121        trainNNFlag2 = true;1122        trainNN(2);1123    });1124    $('#activate2-btn').click(function() {1125        console.log("activate button");1126        activeNNFlag2 = true;1127        $('#activate2-btn').toggleClass("activatedNN");1128        //if leaded NN, turn off1129        if (loadNNFlag) {1130            loadNNFlag = false;1131            $('#load-nn-btn').toggleClass("activatedNN");1132        }1133    });1134    // ************* LOAD TWO EXPORTED NEURAL NET ACTIVATION FUNCTIONS AND WEIGHTS1135    $('#load-nn-btn').click(function() {1136        console.log("load exported NN button");1137        loadNNFlag = true;1138        $('#load-nn-btn').toggleClass("activatedNN");1139        console.log("LOADING MODEL FOR TARGET: " + selectedTargetName + " " + selectedTargetNum);1140        //SHORT RIGHT1141        if(selectedTargetName == 'sh-r-eyes-select'){1142            loadNNData5 = set_RHand_ShortHair_EyesPosition_5Weights(loadNNData5);1143            loadNNData7 = set_RHand_ShortHair_EyesPosition_7Weights(loadNNData7);1144        } else if(selectedTargetName == 'sh-r-mouth-select'){1145            loadNNData5 = set_RHand_ShortHair_MouthPosition_5Weights(loadNNData5);1146            loadNNData7 = set_RHand_ShortHair_MouthPosition_7Weights(loadNNData7);1147        } else if(selectedTargetName == 'sh-r-front-select'){1148            loadNNData5 = set_RHand_ShortHair_FrontPosition_5Weights(loadNNData5);1149            loadNNData7 = set_RHand_ShortHair_FrontPosition_7Weights(loadNNData7);1150        } else if(selectedTargetName == 'sh-r-side-select'){1151            loadNNData5 = set_RHand_ShortHair_SidePosition_5Weights(loadNNData5);1152            loadNNData7 = set_RHand_ShortHair_SidePosition_7Weights(loadNNData7);1153        } else if(selectedTargetName == 'sh-r-top-select'){1154            loadNNData5 = set_RHand_ShortHair_TopPosition_5Weights(loadNNData5);1155            loadNNData7 = set_RHand_ShortHair_TopPosition_7Weights(loadNNData7);1156        } else if(selectedTargetName == 'sh-r-back-select'){1157            loadNNData5 = set_RHand_ShortHair_BackPosition_5Weights(loadNNData5);1158            loadNNData7 = set_RHand_ShortHair_BackPosition_7Weights(loadNNData7);1159        }1160        //SHORT LEFT1161        else if(selectedTargetName == 'sh-l-eyes-select'){1162            loadNNData5 = set_LHand_ShortHair_EyesPosition_5Weights(loadNNData5);1163            loadNNData7 = set_LHand_ShortHair_EyesPosition_7Weights(loadNNData7);1164        } else if(selectedTargetName == 'sh-l-mouth-select'){1165            loadNNData5 = set_LHand_ShortHair_MouthPosition_5Weights(loadNNData5);1166            loadNNData7 = set_LHand_ShortHair_MouthPosition_7Weights(loadNNData7);1167        } else if(selectedTargetName == 'sh-l-front-select'){1168            loadNNData5 = set_LHand_ShortHair_FrontPosition_5Weights(loadNNData5);1169            loadNNData7 = set_LHand_ShortHair_FrontPosition_7Weights(loadNNData7);1170        } else if(selectedTargetName == 'sh-l-side-select'){1171            loadNNData5 = set_LHand_ShortHair_SidePosition_5Weights(loadNNData5);1172            loadNNData7 = set_LHand_ShortHair_SidePosition_7Weights(loadNNData7);1173        } else if(selectedTargetName == 'sh-l-top-select'){1174            loadNNData5 = set_LHand_ShortHair_TopPosition_5Weights(loadNNData5);1175            loadNNData7 = set_LHand_ShortHair_TopPosition_7Weights(loadNNData7);1176        } else if(selectedTargetName == 'sh-l-back-select'){1177            loadNNData5 = set_LHand_ShortHair_BackPosition_5Weights(loadNNData5);1178            loadNNData7 = set_LHand_ShortHair_BackPosition_7Weights(loadNNData7);1179        }1180        //MEDIUM LEFT1181        else if(selectedTargetName == 'md-l-eyes-select'){1182            loadNNData5 = set_LHand_MediumHair_EyesPosition_5Weights(loadNNData5);1183            loadNNData7 = set_LHand_MediumHair_EyesPosition_7Weights(loadNNData7);1184        } else if(selectedTargetName == 'md-l-mouth-select'){1185            loadNNData5 = set_LHand_MediumHair_MouthPosition_5Weights(loadNNData5);1186            loadNNData7 = set_LHand_MediumHair_MouthPosition_7Weights(loadNNData7);1187        } else if(selectedTargetName == 'md-l-front-select'){1188            loadNNData5 = set_LHand_MediumHair_FrontPosition_5Weights(loadNNData5);1189            loadNNData7 = set_LHand_MediumHair_FrontPosition_7Weights(loadNNData7);1190        } else if(selectedTargetName == 'md-l-side-select'){1191            loadNNData5 = set_LHand_MediumHair_SidePosition_5Weights(loadNNData5);1192            loadNNData7 = set_LHand_MediumHair_SidePosition_7Weights(loadNNData7);1193        } else if(selectedTargetName == 'md-l-top-select'){1194            loadNNData5 = set_LHand_MediumHair_TopPosition_5Weights(loadNNData5);1195            loadNNData7 = set_LHand_MediumHair_TopPosition_7Weights(loadNNData7);1196        } else if(selectedTargetName == 'md-l-back-select'){1197            loadNNData5 = set_LHand_MediumHair_BackPosition_5Weights(loadNNData5);1198            loadNNData7 = set_LHand_MediumHair_BackPosition_7Weights(loadNNData7);1199        }1200        //MEDIUM RIGHT1201        else if(selectedTargetName == 'md-r-eyes-select'){1202            loadNNData5 = set_RHand_MediumHair_EyesPosition_5Weights(loadNNData5);1203            loadNNData7 = set_RHand_MediumHair_EyesPosition_7Weights(loadNNData7);1204        } else if(selectedTargetName == 'md-r-mouth-select'){1205            loadNNData5 = set_RHand_MediumHair_MouthPosition_5Weights(loadNNData5);1206            loadNNData7 = set_RHand_MediumHair_MouthPosition_7Weights(loadNNData7);1207        } else if(selectedTargetName == 'md-r-front-select'){1208            loadNNData5 = set_RHand_MediumHair_FrontPosition_5Weights(loadNNData5);1209            loadNNData7 = set_RHand_MediumHair_FrontPosition_7Weights(loadNNData7);1210        } else if(selectedTargetName == 'md-r-side-select'){1211            loadNNData5 = set_RHand_MediumHair_SidePosition_5Weights(loadNNData5);1212            loadNNData7 = set_RHand_MediumHair_SidePosition_7Weights(loadNNData7);1213        } else if(selectedTargetName == 'md-r-top-select'){1214            loadNNData5 = set_RHand_MediumHair_TopPosition_5Weights(loadNNData5);1215            loadNNData7 = set_RHand_MediumHair_TopPosition_7Weights(loadNNData7);1216        } else if(selectedTargetName == 'md-r-back-select'){1217            loadNNData5 = set_RHand_MediumHair_BackPosition_5Weights(loadNNData5);1218            loadNNData7 = set_RHand_MediumHair_BackPosition_7Weights(loadNNData7);1219        }1220        //LONG LEFT1221        else if(selectedTargetName == 'lg-l-eyes-select'){1222            loadNNData5 = set_LHand_LongHair_EyesPosition_5Weights(loadNNData5);1223            loadNNData7 = set_LHand_LongHair_EyesPosition_7Weights(loadNNData7);1224        } else if(selectedTargetName == 'lg-l-mouth-select'){1225            loadNNData5 = set_LHand_LongHair_MouthPosition_5Weights(loadNNData5);1226            loadNNData7 = set_LHand_LongHair_MouthPosition_7Weights(loadNNData7);1227        } else if(selectedTargetName == 'lg-l-front-select'){1228            loadNNData5 = set_LHand_LongHair_FrontPosition_5Weights(loadNNData5);1229            loadNNData7 = set_LHand_LongHair_FrontPosition_7Weights(loadNNData7);1230        } else if(selectedTargetName == 'lg-l-side-select'){1231            loadNNData5 = set_LHand_LongHair_SidePosition_5Weights(loadNNData5);1232            loadNNData7 = set_LHand_LongHair_SidePosition_7Weights(loadNNData7);1233        } else if(selectedTargetName == 'lg-l-top-select'){1234            loadNNData5 = set_LHand_LongHair_TopPosition_5Weights(loadNNData5);1235            loadNNData7 = set_LHand_LongHair_TopPosition_7Weights(loadNNData7);1236        } else if(selectedTargetName == 'lg-l-back-select'){1237            loadNNData5 = set_LHand_LongHair_BackPosition_5Weights(loadNNData5);1238            loadNNData7 = set_LHand_LongHair_BackPosition_7Weights(loadNNData7);1239        }1240        //LONG RIGHT1241        else if(selectedTargetName == 'lg-r-eyes-select'){1242            loadNNData5 = set_RHand_LongHair_EyesPosition_5Weights(loadNNData5);1243            loadNNData7 = set_RHand_LongHair_EyesPosition_7Weights(loadNNData7);1244        } else if(selectedTargetName == 'lg-r-mouth-select'){1245            loadNNData5 = set_RHand_LongHair_MouthPosition_5Weights(loadNNData5);1246            loadNNData7 = set_RHand_LongHair_MouthPosition_7Weights(loadNNData7);1247        } else if(selectedTargetName == 'lg-r-front-select'){1248            loadNNData5 = set_RHand_LongHair_FrontPosition_5Weights(loadNNData5);1249            loadNNData7 = set_RHand_LongHair_FrontPosition_7Weights(loadNNData7);1250        } else if(selectedTargetName == 'lg-r-side-select'){1251            loadNNData5 = set_RHand_LongHair_SidePosition_5Weights(loadNNData5);1252            loadNNData7 = set_RHand_LongHair_SidePosition_7Weights(loadNNData7);1253        } else if(selectedTargetName == 'lg-r-top-select'){1254            loadNNData5 = set_RHand_LongHair_TopPosition_5Weights(loadNNData5);1255            loadNNData7 = set_RHand_LongHair_TopPosition_7Weights(loadNNData7);1256        } else if(selectedTargetName == 'lg-r-back-select'){1257            loadNNData5 = set_RHand_LongHair_BackPosition_5Weights(loadNNData5);1258            loadNNData7 = set_RHand_LongHair_BackPosition_7Weights(loadNNData7);1259        }1260    });1261    /*******************************************************************************************************************1262     ********************************** COLLECT, PRINT, LOAD BUTTON ACTIONS *********************************************1263     ********************************************************************************************************************/1264    /*************** COLLECT SAMPLE - SONSOR AND MODEL DATA - STORE IN GSHEET AND ADD TO NN TRAINING OBJECT *****************/1265    $('#collect-true-1').click(function() {1266        //how many samples for this set?1267        getSamplesFlag = $('input.sample-size').val();1268        getSamplesTypeFlag = 1;1269        console.log("Collect btn NN1T #samples flag: " + getSamplesFlag);1270    });1271    $('#collect-false-1').click(function() {1272        //how many samples for this set?1273        //this flag is applied in the bluetooth data notification function1274        getSamplesFlag = $('input.sample-size').val();1275        getSamplesTypeFlag = 2;1276        console.log("Collect btn NN1F #samples flag: " + getSamplesFlag);1277    });1278    $('#collect-true-2').click(function() {1279        //how many samples for this set?1280        getSamplesFlag = $('input.sample-size').val();1281        //this flag is applied in the bluetooth data notification function1282        getSamplesTypeFlag = 3;1283        console.log("Collect btn NN2T #samples flag: " + getSamplesFlag);1284    });1285    $('#collect-false-2').click(function() {1286        //how many samples for this set?1287        getSamplesFlag = $('input.sample-size').val();1288        //this flag is applied in the bluetooth data notification function1289        getSamplesTypeFlag = 4;1290        console.log("Collect btn NN2F #samples flag: " + getSamplesFlag);1291    });1292    $('#clear-1').click(function() {1293        NN1TrueDataArray = new Array;1294        NN1FalseDataArray = new Array;1295        sensorDataArray = new Array(18).fill(0);1296        sensorDataSession = new Array;1297        updateSampleCountDisplay();1298        $("#dump-print").html("");1299        console.log("Clear NN1 Data");1300    });1301    $('#clear-2').click(function() {1302        NN2TrueDataArray = new Array;1303        NN2FalseDataArray = new Array;1304        sensorDataArray = new Array(18).fill(0);1305        sensorDataSession = new Array;1306        updateSampleCountDisplay();1307        $("#dump-print").html("");1308        console.log("Clear NN2 Data");1309    });1310    $('#export-btn').click(function() {1311        console.log("export1 NN button");1312        //clear everything but key values from stored NN1313        neuralNet.clear();1314        //export optimized weights and activation function1315        var standalone = neuralNet.standalone();1316        //convert to string for parsing1317        standalone = standalone.toString();1318        console.log(standalone);1319        $("#dump-print").html(standalone);1320        $("#dump-print").addClass("active-print");1321    });1322    $('#export2-btn').click(function() {1323        console.log("export2 NN button");1324        //clear everything but key values from stored NN1325        neuralNet2.clear();1326        //export optimized weights and activation function1327        var standalone = neuralNet2.standalone();1328        //convert to string for parsing1329        standalone = standalone.toString();1330        console.log(standalone);1331        $("#dump-print").html(standalone);1332        $("#dump-print").addClass("active-print");1333    });1334        //print sensor data to browser at bottom of app screen1335    $('#print-btn').click(function() {1336        console.log("print button");1337        $("#dump-print").html("");1338    var dataVarNames=["sh_r_eyes_data", "sh_r_mouth_data", "sh_r_front_data", "sh_r_side_data", "sh_r_top_data", "sh_r_back_data", "sh_r_off_data",1339                        "sh_l_eyes_data", "sh_l_mouth_data", "sh_l_front_data", "sh_l_side_data", "sh_l_top_data", "sh_l_back_data", "sh_l_off_data",1340                        "md_r_eyes_data", "md_r_mouth_data", "md_r_front_data", "md_r_side_data", "md_r_top_data", "md_r_back_data", "md_r_off_data",1341                        "md_l_eyes_data", "md_l_mouth_data", "md_l_front_data", "md_l_side_data", "md_l_top_data", "md_l_back_data", "md_l_off_data",1342                        "lg_r_eyes_data", "lg_r_mouth_data", "lg_r_front_data", "lg_r_side_data", "lg_r_top_data", "lg_r_back_data", "lg_r_off_data",1343                        "lg_l_eyes_data", "lg_l_mouth_data", "lg_l_front_data", "lg_l_side_data", "lg_l_top_data", "lg_l_back_data", "lg_l_off_data"];1344        for( var i=0; i < masterDataArray.length; i++){1345            $("#dump-print").append("<p>var " + dataVarNames[i] + "=" + JSON.stringify(masterDataArray[i]) + ";</p>" );1346            console.log(dataVarNames[i] + " SESSION DATA: " + masterDataArray[i]);1347        }1348     //   $("#dump-print").html(JSON.stringify(sensorDataSession));1349        $("#dump-print").addClass("active-print");1350       // console.log("SENSOR SESSIONS DATA: " + sensorDataSession);1351    });1352    //load data from js file (JSON or JS object) into sensor session data1353    $('#load-btn').click(function() {1354        console.log("load button");1355        masterDataArray[0] = sh_r_eyes_data1356        masterDataArray[1] = sh_r_mouth_data;1357        masterDataArray[2] = sh_r_front_data;1358        masterDataArray[3] = sh_r_side_data;1359        masterDataArray[4] = sh_r_top_data;1360        masterDataArray[5] = sh_r_back_data;1361        masterDataArray[6] = sh_r_off_data;1362        masterDataArray[7] = sh_l_eyes_data;1363        masterDataArray[8] = sh_l_mouth_data;1364        masterDataArray[9] = sh_l_front_data;1365        masterDataArray[10] = sh_l_side_data;1366        masterDataArray[11] = sh_l_top_data;1367        masterDataArray[12] = sh_l_back_data;1368        masterDataArray[13] = sh_l_off_data;1369        masterDataArray[14] = md_r_eyes_data;1370        masterDataArray[15] = md_r_mouth_data;1371        masterDataArray[16] = md_r_front_data;1372        masterDataArray[17] = md_r_side_data;1373        masterDataArray[18] = md_r_top_data;1374        masterDataArray[19] = md_r_back_data;1375        masterDataArray[20] = md_r_off_data;1376        masterDataArray[21] = md_l_eyes_data;1377        masterDataArray[22] = md_l_mouth_data;1378        masterDataArray[23] = md_l_front_data;1379        masterDataArray[24] = md_l_side_data;1380        masterDataArray[25] = md_l_top_data;1381        masterDataArray[26] = md_l_back_data;1382        masterDataArray[27] = md_l_off_data;1383        masterDataArray[28] = lg_r_eyes_data;1384        masterDataArray[29] = lg_r_mouth_data;1385        masterDataArray[30] = lg_r_front_data;1386        masterDataArray[31] = lg_r_side_data;1387        masterDataArray[32] = lg_r_top_data;1388        masterDataArray[33] = lg_r_back_data;1389        masterDataArray[34] = lg_r_off_data;1390        masterDataArray[35] = lg_l_eyes_data;1391        masterDataArray[36] = lg_l_mouth_data;1392        masterDataArray[37] = lg_l_front_data;1393        masterDataArray[38] = lg_l_side_data;1394        masterDataArray[39] = lg_l_top_data;1395        masterDataArray[40] = lg_l_back_data;1396        masterDataArray[41] = lg_l_off_data;1397     /*   masterDataArray[28] = $.merge( lg_r_eyes_data, md_r_eyes_data);1398        masterDataArray[29] = $.merge( lg_r_mouth_data, md_r_mouth_data);1399        masterDataArray[30] = $.merge( lg_r_front_data, md_r_front_data);1400        masterDataArray[31] = $.merge( lg_r_side_data, md_r_side_data);1401        masterDataArray[32] = $.merge( lg_r_top_data, md_r_top_data);1402        masterDataArray[33] = $.merge( lg_r_back_data, md_r_back_data);1403        masterDataArray[34] = $.merge( lg_r_off_data, md_r_off_data);1404        masterDataArray[35] = $.merge( lg_l_eyes_data, md_l_eyes_data);1405        masterDataArray[36] = $.merge( lg_l_mouth_data, md_l_mouth_data);1406        masterDataArray[37] = $.merge( lg_l_front_data, md_l_front_data);1407        masterDataArray[38] = $.merge( lg_l_side_data, md_l_side_data);1408        masterDataArray[39] = $.merge( lg_l_top_data, md_l_top_data);1409        masterDataArray[40] = $.merge( lg_l_back_data, md_l_back_data);1410        masterDataArray[41] = $.merge( lg_l_off_data, md_l_off_data); */1411        //update data count labels1412        for( var i=0; i < masterDataArray.length; i++){1413            $( "span[data='" + i + "']" ).html(masterDataArray[i].length);1414        }1415    });1416}); // end on document load...

TreeGrid.js

Source:TreeGrid.js

1describe("Ext.tree.TreeGrid", function() {2    var TreeGridItem = Ext.define(null, {3            extend: 'Ext.data.Model',4            fields: ['f1', 'f2'],5            proxy: {6                type: 'memory'7            }8        }),9        tree,10        view,11        store,12        recordCount,13        treeData = {14            f1: 'root1',15            f2: 'root.a',16            // Add cls. Tests must not throw errors with this present.17            cls: 'test-EXTJS-16367',18            children: [{19                f1: '1',20                f2: 'a',21                children: [{22                    f1: '1.1',23                    f2: 'a.a',24                    leaf: true25                }, {26                    f1: '1.2',27                    f2: 'a.b',28                    leaf: true29                }, {30                    f1: '1.3',31                    f2: 'a.c',32                    leaf: true33                }, {34                    f1: '1.4',35                    f2: 'a.d',36                    leaf: true37                }]38            }, {39                f1: '2',40                f2: 'b',41                children: [{42                    f1: '2.1',43                    f2: 'b.a',44                    leaf: true45                }, {46                    f1: '2.2',47                    f2: 'b.b',48                    leaf: true49                }, {50                    f1: '2.3',51                    f2: 'b.c',52                    leaf: true53                }, {54                    f1: '2.4',55                    f2: 'b.d',56                    leaf: true57                }]58            }, {59                f1: '3',60                f2: 'c',61                children: [{62                    f1: '3.1',63                    f2: 'c.a',64                    leaf: true65                }, {66                    f1: '3.2',67                    f2: 'c.b',68                    leaf: true69                }, {70                    f1: '3.3',71                    f2: 'c.c',72                    leaf: true73                }, {74                    f1: '3.4',75                    f2: 'c.d',76                    leaf: true77                }]78            }, {79                f1: '4',80                f2: 'd',81                children: [{82                    f1: '4.1',83                    f2: 'd.a',84                    leaf: true85                }, {86                    f1: '4.2',87                    f2: 'd.b',88                    leaf: true89                }, {90                    f1: '4.3',91                    f2: 'd.c',92                    leaf: true93                }, {94                    f1: '4.4',95                    f2: 'd.d',96                    leaf: true97                }]98            }, {99                f1: '5',100                f2: 'e',101                children: [{102                    f1: '5.1',103                    f2: 'e.a',104                    leaf: true105                }, {106                    f1: '5.2',107                    f2: 'e.b',108                    leaf: true109                }, {110                    f1: '5.3',111                    f2: 'e.c',112                    leaf: true113                }, {114                    f1: '5.4',115                    f2: 'e.d',116                    leaf: true117                }]118            }, {119                f1: '6',120                f2: 'f',121                children: [{122                    f1: '6.1',123                    f2: 'f.a',124                    leaf: true125                }, {126                    f1: '6.2',127                    f2: 'f.b',128                    leaf: true129                }, {130                    f1: '6.3',131                    f2: 'f.c',132                    leaf: true133                }, {134                    f1: '6.4',135                    f2: 'f.d',136                    leaf: true137                }]138            }]139        },140        synchronousLoad = true,141        treeStoreLoad = Ext.data.TreeStore.prototype.load,142        loadStore;143    144    function makeTreeGrid(cfg, storeCfg) {145        tree = new Ext.tree.Panel(Ext.apply({146            animate: false,147            renderTo: Ext.getBody(),148            store: store = new Ext.data.TreeStore(Ext.apply({149                model: TreeGridItem,150                root: Ext.clone(treeData)151            }, storeCfg)),152            trailingBufferZone: 1000,153            leadingBufferZone: 1000,154            width: 200,155            columns: [{156                xtype: 'treecolumn',157                text: 'F1',158                dataIndex: 'f1',159                width: 100160            }, {161                text: 'F2',162                dataIndex: 'f2',163                flex: 1164            }]165        }, cfg));166        view = tree.getView();167    }168    beforeEach(function() {169        // Override so that we can control asynchronous loading170        loadStore = Ext.data.TreeStore.prototype.load = function() {171            treeStoreLoad.apply(this, arguments);172            if (synchronousLoad) {173                this.flushLoad.apply(this, arguments);174            }175            return this;176        };177    });178    afterEach(function(){179        // Undo the overrides.180        Ext.data.TreeStore.prototype.load = treeStoreLoad;181        Ext.destroy(tree);182    });183    184    describe('Model mutation', function() {185        it('should not have to render a whole row, it should update innerHTML of cell', function() {186            makeTreeGrid();187            // Test cls config188            expect(view.getCellByPosition({row:0, column: 0}).hasCls('test-EXTJS-16367')).toBe(true);189            var createRowSpy = spyOn(view, 'createRowElement').andCallThrough();190            store.getAt(0).set({191                f1: 'ploot',192                f2: 'gronk'193            });194            // MUST not have created a bew row, we must have just updated the text within the cell195            expect(createRowSpy).not.toHaveBeenCalled();196        });197    });198    describe('autoloading', function() {199        it('should not autoload the store if the root is visible', function() {200            var loadCount = 0;201            // rootVisible defaults to true, so no autoload202            makeTreeGrid({203                columns: [{204                    xtype: 'treecolumn',205                    text: 'F1',206                    dataIndex: 'f1',207                    width: 100208                }],209                    store: {210                    listeners: {211                        load: function() {212                            loadCount++;213                        }214                    }215                }216            });217            expect(loadCount).toBe(0);218        });219        it('should not autoload the store if the root is visible and there is a locked column', function() {220            var loadCount = 0;221            // rootVisible defaults to true, so no autoload222            makeTreeGrid({223                columns: [{224                    xtype: 'treecolumn',225                    text: 'F1',226                    dataIndex: 'f1',227                    width: 100,228                    locked: true229                }],230                store: {231                    listeners: {232                        load: function() {233                            loadCount++;234                        }235                    }236                }237            });238            expect(loadCount).toBe(0);239        });240        it('should autoload the store if the root is visible', function() {241            var loadCount = 0;242            // rootVisible set to false, so autoload so that user sees the tree content243            makeTreeGrid({244                rootVisible: false,245                columns: [{246                    xtype: 'treecolumn',247                    text: 'F1',248                    dataIndex: 'f1',249                    width: 100250                }],251                store: {252                    proxy: 'memory',253                    listeners: {254                        load: function() {255                            loadCount++;256                        }257                    }258                }259            });260            expect(loadCount).toBe(1);261        });262        it('should autoload the store if the root is visible and there is a locked column', function() {263            var loadCount = 0;264            // rootVisible set to false, so autoload so that user sees the tree content265            makeTreeGrid({266                rootVisible: false,267                columns: [{268                    xtype: 'treecolumn',269                    text: 'F1',270                    dataIndex: 'f1',271                    width: 100,272                    locked: true273                }],274                store: {275                    proxy: 'memory',276                    listeners: {277                        load: function() {278                            loadCount++;279                        }280                    }281                }282            });283            expect(loadCount).toBe(1);284        });285    });286    describe("Buffered rendering", function() {287        var rootNode;288        beforeEach(function() {289            makeTreeGrid({290                height: 45,291                plugins: Ext.create('Ext.grid.plugin.BufferedRenderer', {292                    trailingBufferZone: 1,293                    leadingBufferZone: 1294                })295            });296            tree.expandAll();297            recordCount = tree.view.store.getCount();298            rootNode = tree.getRootNode();299        });300        it("should not render every node", function() {301            expect(recordCount).toEqual(31);302            // The view's Composite element should only contain the visible rows plus buffer zones.303            // Should be less than the total node count in the Tree structure.304            expect(tree.view.all.getCount()).toBeLessThan(recordCount);305        });306        it("should not not scroll upon node expand", function() {307            tree.collapseAll();308            rootNode.expand();309            tree.view.setScrollY(40);310            tree.getRootNode().childNodes[1].expand();311            // Expanding a node should not scroll.312            expect(tree.view.getScrollY()).toEqual(40);313        });314    });315    describe('buffered rendering with locking and rootVisible: false', function() {316        var rootNode;317        beforeEach(function() {318                makeTreeGrid({319                    renderTo: Ext.getBody(),320                    height: 120,321                    store: new Ext.data.TreeStore({322                        model: TreeGridItem,323                        root: {324                            f1: 'Root',325                            f2: 'root',326                            children: [{327                                f1: 'c0',328                                f2: 'c0',329                                leaf: true330                            }, {331                                f1: 'c1',332                                f2: 'c1',333                                leaf: true334                            }, {335                                f1: 'c2',336                                f2: 'c2',337                                leaf: true338                            }]339                        }340                    }),341                    plugins: Ext.create('Ext.grid.plugin.BufferedRenderer', {342                        trailingBufferZone: 1,343                        leadingBufferZone: 1344                    }),345                    columns: [{346                        xtype: 'treecolumn',347                        text: 'F1',348                        dataIndex: 'f1',349                        width: 100,350                        locked: true351                    }, {352                        text: 'F2',353                        dataIndex: 'f2',354                        flex: 1355                    }],356                    rootVisible: false357                });358                recordCount = tree.lockedGrid.view.store.getCount();359                rootNode = tree.getRootNode();360        });361        it('should work when inserting a node at the top', function() {362            expect(tree.lockedGrid.view.all.getCount()).toEqual(3);363            expect(tree.normalGrid.view.all.getCount()).toEqual(3);364            rootNode.insertBefore({text:'Top'}, rootNode.childNodes[0]);365            expect(tree.lockedGrid.view.all.getCount()).toEqual(4);366            expect(tree.normalGrid.view.all.getCount()).toEqual(4);367        });368    });369    describe("Buffered rendering and locking", function() {370        var rootNode;371        beforeEach(function() {372            makeTreeGrid({373                height: 45,374                plugins: Ext.create('Ext.grid.plugin.BufferedRenderer', {375                    trailingBufferZone: 1,376                    leadingBufferZone: 1377                }),378                columns: [{379                    xtype: 'treecolumn',380                    text: 'F1',381                    dataIndex: 'f1',382                    width: 100,383                    locked: true384                }, {385                    text: 'F2',386                    dataIndex: 'f2',387                    flex: 1388                }]389            });390            tree.expandAll();391            recordCount = tree.lockedGrid.view.store.getCount();392            rootNode = tree.getRootNode();393        });394        it("should not render every node", function() {395            var lockedTree = tree.lockedGrid,396                normalGrid = tree.normalGrid,397                viewSize = lockedTree.view.all.getCount();398            expect(recordCount).toEqual(31);399            // The view's Composite element should only contain the visible rows plus buffer zones.400            // Should be less than the total node count in the Tree structure.401            expect(viewSize).toBeLE(recordCount);402            expect(normalGrid.view.all.getCount()).toEqual(viewSize);403        });404        it('should sync scroll positions between the two sides', function() {405            var lockedTree = tree.lockedGrid,406                normalGrid = tree.normalGrid,407                lockedView = lockedTree.view,408                normalView = normalGrid.view;409            tree.collapseAll();410            rootNode.expand();411            normalView.setScrollY(30);412            waits(200); // Wait for the scroll listener (deferred to next animation Frame)413            runs(function() {414                expect(lockedView.getScrollY()).toEqual(30);415                // Now, at 120px high, the entire tree is rendered, scrolling will not triggert action by the buffered renderer416                // Scrolling should still sync417                tree.setHeight(120);418                normalView.setScrollY(45);419                waits(200); // Wait for the scroll listener (deferred to next animation Frame)420                runs(function() {421                    expect(lockedView.getScrollY()).toEqual(45);422                    rootNode.childNodes[2].expand();423                    // Root node, its 6 children, and child[2]'s 4 children: 11 records in NodeStore424                    expect(normalView.store.getCount()).toEqual(11);425                    // But scroll position should not change426                    expect(lockedView.el.dom.scrollTop).toEqual(45);427                });428            });429        });430    });431    432    describe('reconfigure', function() {433        it('should allow reconfigure', function() {434            var cols = [{435                xtype: 'treecolumn',436                text: 'Task',437                flex: 1,438                dataIndex: 'task'439            }, {440                text: 'URL',441                flex: 1,442                sortable: false,443                dataIndex: 'url'444            }];445            var cols2 = [{446                xtype: 'treecolumn',447                text: 'New Task',448                flex: 1,449                dataIndex: 'new_task'450            }, {451                text: 'New URL',452                flex: 1,453                sortable: false,454                dataIndex: 'new_url'455            }];456            Ext.define('ReconfigureTestTask', {457                extend: 'Ext.data.Model',458                fields: [{459                    name: 'task',460                    type: 'string'461                }, {462                    name: 'url',463                    type: 'string'464                }]465            });466            Ext.define('ReconfigureTestNewTask', {467                extend: 'Ext.data.Model',468                fields: [{469                    name: 'new_task',470                    type: 'string'471                }, {472                    name: 'new_url',473                    type: 'string'474                }]475            });476            var store = Ext.create('Ext.data.TreeStore', {477                model: 'ReconfigureTestTask',478                root: {479                    expanded: true,480                    children: [{481                        task: 'task1',482                        url: 'url1',483                        expanded: true,484                        children: [{485                            task: 'task1.1',486                            url: 'url1.1',487                            leaf: true488                        }]489                    }, {490                        task: 'task2',491                        url: 'url2',492                        expanded: true,493                        children: [{494                            task: 'task2.1',495                            url: 'url2.1',496                            leaf: true497                        }]498                    }]499                }500            });501            var store2 = Ext.create('Ext.data.TreeStore', {502                model: 'ReconfigureTestNewTask',503                root: {504                    expanded: true,505                    children: [{506                        new_task: 'new-task1',507                        new_url: 'new-url1',508                        expanded: true,509                        children: [{510                            new_task: 'new-task1.1',511                            new_url: 'new-url1.1',512                            leaf: true513                        }]514                    }, {515                        new_task: 'new-task2',516                        new_url: 'new-url2',517                        expanded: true,518                        children: [{519                            new_task: 'new-task2.1',520                            new_url: 'new-url2.1',521                            leaf: true522                        }]523                    }]524                }525            });526            var myTree = Ext.create('Ext.tree.Panel', {527                title: 'treegrid',528                width: 600,529                height: 300,530                renderTo: Ext.getBody(),531                collapsible: true,532                rootVisible: false,533                useArrows: true,534                store: store,535                multiSelect: true,536                columns: cols537            }),538            root = myTree.getRootNode();539            expect(root.childNodes[0].data.task).toEqual('task1');540            expect(root.childNodes[0].data.url).toEqual('url1');541            expect(root.childNodes[0].childNodes[0].data.task).toEqual('task1.1');542            expect(root.childNodes[0].childNodes[0].data.url).toEqual('url1.1');543            expect(root.childNodes[1].data.task).toEqual('task2');544            expect(root.childNodes[1].data.url).toEqual('url2');545            expect(root.childNodes[1].childNodes[0].data.task).toEqual('task2.1');546            expect(root.childNodes[1].childNodes[0].data.url).toEqual('url2.1');547            myTree.reconfigure(store2, cols2);548            root = myTree.getRootNode();549            expect(root.childNodes[0].data.new_task).toEqual('new-task1');550            expect(root.childNodes[0].data.new_url).toEqual('new-url1');551            expect(root.childNodes[0].childNodes[0].data.new_task).toEqual('new-task1.1');552            expect(root.childNodes[0].childNodes[0].data.new_url).toEqual('new-url1.1');553            expect(root.childNodes[1].data.new_task).toEqual('new-task2');554            expect(root.childNodes[1].data.new_url).toEqual('new-url2');555            expect(root.childNodes[1].childNodes[0].data.new_task).toEqual('new-task2.1');556            expect(root.childNodes[1].childNodes[0].data.new_url).toEqual('new-url2.1');557            558            myTree.destroy();559            Ext.undefine('ReconfigureTestTask');560            Ext.undefine('ReconfigureTestNewTask');561        });562    });563    ...

sparse_feature_cross_op_test.py

Source:sparse_feature_cross_op_test.py

1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests for tf.contrib.layers.sparse_feature_cross."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import numpy20from tensorflow.contrib import layers21from tensorflow.contrib.layers.python.ops import sparse_feature_cross_op22from tensorflow.python.client import session23from tensorflow.python.framework import constant_op24from tensorflow.python.framework import dtypes25from tensorflow.python.framework import sparse_tensor26from tensorflow.python.ops import sparse_ops27from tensorflow.python.platform import test28class SparseCrossOpTest(test.TestCase):29  def test_simple(self):30    """Tests a simple scenario.31    """32    op = sparse_feature_cross_op.sparse_feature_cross([33        self._sparse_tensor([['batch1-FC1-F1'],34                             ['batch2-FC1-F1', 'batch2-FC1-F2']]),35        self._sparse_tensor([['batch1-FC2-F1'],36                             ['batch2-FC2-F1', 'batch2-FC2-F2']])37    ])38    expected_out = self._sparse_tensor([['batch1-FC1-F1_X_batch1-FC2-F1'], [39        'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',40        'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'41    ]])42    with self.test_session() as sess:43      self._assert_sparse_tensor_equals(expected_out, sess.run(op))44  def test_dense(self):45    """Tests only dense inputs.46    """47    op = sparse_feature_cross_op.sparse_feature_cross([48        constant_op.constant([['batch1-FC1-F1', 'batch1-FC1-F2'],49                              ['batch2-FC1-F1', 'batch2-FC1-F2']],50                             dtypes.string),51        constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],52                              ['batch2-FC2-F1', 'batch2-FC2-F2']],53                             dtypes.string),54    ])55    expected_out = self._sparse_tensor([[56        'batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2',57        'batch1-FC1-F2_X_batch1-FC2-F1', 'batch1-FC1-F2_X_batch1-FC2-F2'58    ], [59        'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',60        'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'61    ]])62    with self.test_session() as sess:63      self._assert_sparse_tensor_equals(expected_out, sess.run(op))64  def test_integer_mixed_string_sparse(self):65    """Tests mixed type."""66    op = sparse_feature_cross_op.sparse_feature_cross([67        self._sparse_tensor([[11], [333, 55555]]),68        self._sparse_tensor([['batch1-FC2-F1'],69                             ['batch2-FC2-F1', 'batch2-FC2-F2']])70    ])71    expected_out = self._sparse_tensor([['11_X_batch1-FC2-F1'], [72        '333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2', '55555_X_batch2-FC2-F1',73        '55555_X_batch2-FC2-F2'74    ]])75    with self.test_session() as sess:76      self._assert_sparse_tensor_equals(expected_out, sess.run(op))77  def test_integer_mixed_string_dense(self):78    """Tests mixed dense inputs.79    """80    op = sparse_feature_cross_op.sparse_feature_cross([81        constant_op.constant([[11, 333], [55555, 999999]], dtypes.int64),82        constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],83                              ['batch2-FC2-F1', 'batch2-FC2-F2']],84                             dtypes.string),85    ])86    expected_out = self._sparse_tensor([[87        '11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2', '333_X_batch1-FC2-F1',88        '333_X_batch1-FC2-F2'89    ], [90        '55555_X_batch2-FC2-F1', '55555_X_batch2-FC2-F2',91        '999999_X_batch2-FC2-F1', '999999_X_batch2-FC2-F2'92    ]])93    with self.test_session() as sess:94      self._assert_sparse_tensor_equals(expected_out, sess.run(op))95  def test_sparse_cross_dense(self):96    """Tests sparse and dense inputs.97    """98    op = sparse_feature_cross_op.sparse_feature_cross([99        self._sparse_tensor([['batch1-FC1-F1'],100                             ['batch2-FC1-F1', 'batch2-FC1-F2']]),101        constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],102                              ['batch2-FC2-F1', 'batch2-FC2-F2']],103                             dtypes.string),104    ])105    expected_out = self._sparse_tensor(106        [['batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2'], [107            'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',108            'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'109        ]])110    with self.test_session() as sess:111      self._assert_sparse_tensor_equals(expected_out, sess.run(op))112  def test_integer_sparse_input(self):113    """Tests mixed type sparse and dense inputs."""114    op = sparse_feature_cross_op.sparse_feature_cross([115        self._sparse_tensor([[11], [333, 5555]]),116        constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],117                              ['batch2-FC2-F1', 'batch2-FC2-F2']],118                             dtypes.string),119    ])120    expected_out = self._sparse_tensor(121        [['11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2'], [122            '333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2',123            '5555_X_batch2-FC2-F1', '5555_X_batch2-FC2-F2'124        ]])125    with self.test_session() as sess:126      self._assert_sparse_tensor_equals(expected_out, sess.run(op))127  def test_permutation_3x3x3(self):128    """Tests 3x3x3 permutation.129    """130    op = sparse_feature_cross_op.sparse_feature_cross([131        self._sparse_tensor(132            [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]),133        self._sparse_tensor(134            [['batch1-FC2-F1', 'batch1-FC2-F2', 'batch1-FC2-F3']]),135        self._sparse_tensor(136            [['batch1-FC3-F1', 'batch1-FC3-F2', 'batch1-FC3-F3']])137    ])138    expected_out = self._sparse_tensor([[139        'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1',140        'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2',141        'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F3',142        'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F1',143        'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F2',144        'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F3',145        'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F1',146        'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F2',147        'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F3',148        'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1',149        'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2',150        'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F3',151        'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F1',152        'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F2',153        'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F3',154        'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F1',155        'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F2',156        'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F3',157        'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1',158        'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2',159        'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F3',160        'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F1',161        'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F2',162        'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F3',163        'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F1',164        'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F2',165        'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F3'166    ]])167    with self.test_session() as sess:168      self._assert_sparse_tensor_equals(expected_out, sess.run(op))169  def test_permutation_3x1x2(self):170    """Tests 3x1x2 permutation.171    """172    op = sparse_feature_cross_op.sparse_feature_cross([173        self._sparse_tensor(174            [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]),175        self._sparse_tensor([['batch1-FC2-F1']]),176        self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']])177    ])178    expected_out = self._sparse_tensor([[179        'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1',180        'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2',181        'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1',182        'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2',183        'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1',184        'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2'185    ]])186    with self.test_session() as sess:187      self._assert_sparse_tensor_equals(expected_out, sess.run(op))188  def test_large_batch(self):189    """Tests with large batch size to force multithreding.190    """191    batch_size = 5000192    col1 = []193    col2 = []194    col3 = []195    for b in range(batch_size):196      col1.append(197          ['batch%d-FC1-F1' % b, 'batch%d-FC1-F2' % b, 'batch%d-FC1-F3' % b])198      col2.append(['batch%d-FC2-F1' % b])199      col3.append(['batch%d-FC3-F1' % b, 'batch%d-FC3-F2' % b])200    op = sparse_feature_cross_op.sparse_feature_cross([201        self._sparse_tensor(col1), self._sparse_tensor(col2),202        self._sparse_tensor(col3)203    ])204    col_out = []205    for b in range(batch_size):206      col_out.append([207          'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b),208          'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b),209          'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b),210          'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b),211          'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b),212          'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b)213      ])214    expected_out = self._sparse_tensor(col_out)215    with self.test_session() as sess:216      self._assert_sparse_tensor_equals(expected_out, sess.run(op))217  def test_one_column_empty(self):218    """Tests when one column is empty.219    The crossed tensor should be empty.220    """221    op = sparse_feature_cross_op.sparse_feature_cross([222        self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']]),223        self._sparse_tensor([], 1),224        self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']])225    ])226    with self.test_session() as sess:227      self._assert_sparse_tensor_empty(sess.run(op))228  def test_some_columns_empty(self):229    """Tests when more than one columns are empty.230    Cross for the corresponding batch should be empty.231    """232    op = sparse_feature_cross_op.sparse_feature_cross([233        self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']], 2),234        self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1']], 2),235        self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']], 2)236    ])237    expected_out = self._sparse_tensor([[238        'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1',239        'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2',240        'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1',241        'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2'242    ]], 2)243    with self.test_session() as sess:244      self._assert_sparse_tensor_equals(expected_out, sess.run(op))245  def test_all_columns_empty(self):246    """Tests when all columns are empty.247    The crossed tensor should be empty.248    """249    op = sparse_feature_cross_op.sparse_feature_cross([250        self._sparse_tensor([]), self._sparse_tensor([]),251        self._sparse_tensor([])252    ])253    with self.test_session() as sess:254      self._assert_sparse_tensor_empty(sess.run(op))255  def test_hashed_output_zero_bucket(self):256    """Tests a simple scenario.257    """258    op = sparse_feature_cross_op.sparse_feature_cross(259        [260            self._sparse_tensor([['batch1-FC1-F1']]),261            self._sparse_tensor([['batch1-FC2-F1']]),262            self._sparse_tensor([['batch1-FC3-F1']])263        ],264        hashed_output=True)265    # Check actual hashed output to prevent unintentional hashing changes.266    expected_out = self._sparse_tensor([[3735511728867393167]])267    with self.test_session() as sess:268      self._assert_sparse_tensor_equals(expected_out, sess.run(op))269  def test_hashed_output_zero_bucket_v2(self):270    """Tests a simple scenario.271    """272    op = sparse_feature_cross_op.sparse_feature_cross(273        [274            self._sparse_tensor([['batch1-FC1-F1']]),275            self._sparse_tensor([['batch1-FC2-F1']]),276            self._sparse_tensor([['batch1-FC3-F1']])277        ],278        hashed_output=True,279        hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)280    # Check actual hashed output to prevent unintentional hashing changes.281    expected_out = self._sparse_tensor([[1971693436396284976]])282    with self.test_session() as sess:283      self._assert_sparse_tensor_equals(expected_out, sess.run(op))284  # TODO(sibyl-Aix6ihai): Add benchmark to compare Hashed vs Non-hashed.285  def test_hashed_output(self):286    """Tests a simple scenario.287    """288    op = sparse_feature_cross_op.sparse_feature_cross(289        [290            self._sparse_tensor([['batch1-FC1-F1']]),291            self._sparse_tensor([['batch1-FC2-F1']]),292            self._sparse_tensor([['batch1-FC3-F1']])293        ],294        hashed_output=True,295        num_buckets=100)296    # Check actual hashed output to prevent unintentional hashing changes.297    expected_out = self._sparse_tensor([[74]])298    with self.test_session() as sess:299      self._assert_sparse_tensor_equals(expected_out, sess.run(op))300  def test_hashed_output_v2(self):301    """Tests a simple scenario.302    """303    op = sparse_feature_cross_op.sparse_feature_cross(304        [305            self._sparse_tensor([['batch1-FC1-F1']]),306            self._sparse_tensor([['batch1-FC2-F1']]),307            self._sparse_tensor([['batch1-FC3-F1']])308        ],309        hashed_output=True,310        num_buckets=100,311        hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)312    # Check actual hashed output to prevent unintentional hashing changes.313    expected_out = self._sparse_tensor([[83]])314    with self.test_session() as sess:315      self._assert_sparse_tensor_equals(expected_out, sess.run(op))316  def test_hashed_output_v1_has_collision(self):317    """Tests the old version of the fingerprint concatenation has collisions.318    """319    # The last 10 bits of 359 and 1024+359 are identical.320    # As a result, all the crosses collide.321    t1 = constant_op.constant([[359], [359 + 1024]])322    t2 = constant_op.constant([list(range(10)), list(range(10))])323    cross = sparse_feature_cross_op.sparse_feature_cross(324        [t2, t1], hashed_output=True, num_buckets=1024)325    cross_dense = sparse_ops.sparse_tensor_to_dense(cross)326    with session.Session():327      values = cross_dense.eval()328      self.assertTrue(numpy.equal(values[0], values[1]).all())329  def test_hashed_output_v2_has_no_collision(self):330    """Tests the new version of the fingerprint concatenation has no collisions.331    """332    # Although the last 10 bits of 359 and 1024+359 are identical.333    # As a result, all the crosses shouldn't collide.334    t1 = constant_op.constant([[359], [359 + 1024]])335    t2 = constant_op.constant([list(range(10)), list(range(10))])336    cross = sparse_feature_cross_op.sparse_feature_cross(337        [t2, t1],338        hashed_output=True,339        num_buckets=1024,340        hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)341    cross_dense = sparse_ops.sparse_tensor_to_dense(cross)342    with session.Session():343      values = cross_dense.eval()344      self.assertTrue(numpy.not_equal(values[0], values[1]).all())345  def test_hashed_3x1x2(self):346    """Tests 3x1x2 permutation with hashed output.347    """348    op = sparse_feature_cross_op.sparse_feature_cross(349        [350            self._sparse_tensor(351                [['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]),352            self._sparse_tensor([['batch1-FC2-F1']]),353            self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']])354        ],355        hashed_output=True,356        num_buckets=1000)357    with self.test_session() as sess:358      out = sess.run(op)359      self.assertEqual(6, len(out.values))360      self.assertAllEqual([[0, i] for i in range(6)], out.indices)361      self.assertTrue(all(x < 1000 and x >= 0 for x in out.values))362      all_values_are_different = len(out.values) == len(set(out.values))363      self.assertTrue(all_values_are_different)364  def _assert_sparse_tensor_empty(self, sp):365    self.assertEquals(0, sp.indices.size)366    self.assertEquals(0, sp.values.size)367    # TODO(zakaria): check if we can ignore the first dim of the shape.368    self.assertEquals(0, sp.dense_shape[1])369  def _assert_sparse_tensor_equals(self, sp1, sp2):370    self.assertAllEqual(sp1.indices.eval(), sp2.indices)371    self.assertAllEqual(sp1.values.eval(), sp2.values)372    self.assertAllEqual(sp1.dense_shape.eval(), sp2.dense_shape)373  def _sparse_tensor(self, data, batch_size=-1):374    """Generates a SparseTensor.375    Args:376      data: Should be a list of list of strings or int64. Each item of the outer377          list represents a batch. Each item of the batch is a feature of a378          specific feature column.379      batch_size: optional batch size, especially for cases when data has no380          entry for some batches.381    Returns:382     A SparseTensor.383    """384    indices = []385    values = []386    max_col_count = 0387    for batch, batch_ix in zip(data, range(len(data))):388      for column, column_ix in zip(batch, range(len(batch))):389        indices.append([batch_ix, column_ix])390        values.append(column)391        max_col_count = max(max_col_count, column_ix + 1)392    shape = [batch_size if batch_size != -1 else len(data), max_col_count]393    value_type = (dtypes.string if not values or isinstance(values[0], str) else394                  dtypes.int64)395    return sparse_tensor.SparseTensor(396        constant_op.constant(indices, dtypes.int64, [len(indices), 2]),397        constant_op.constant(values, value_type, [len(indices)]),398        constant_op.constant(shape, dtypes.int64))399if __name__ == '__main__':...

general.js

Source:general.js

1truncate_constants: {2	options = {3		features	: "ASTREL"4	};5	input: {6		function chunkData() { 7			var x = "very long string, very long string, very long string, very long string, very long string, very long string, very long string, very long string, very long string, very long string, very long string, very long string";8		}9	}10	expect: {11		'{12			"query":[13			{"a": 0,	"b": 1,	"f2": ":VarDef[1]-String"}14			],15			"assign":[16			{"v": 0,	"inf": "x"},17			{"v": 1,	"giv": "very%20long%20string%2C%20very%20long%20string%2C%20very%20long%20string%2C%20very%20long%20"}18			]19		}'20	}21}22escape_constants: {23	options = {24		features	: "ASTREL"25	};26	input: {27		function chunkData() { 28			var x = '"quoted text"';29		}30	}31	expect: {32		'{33			"query":[34			{"a": 0,	"b": 1,	"f2": ":VarDef[1]-String"}35			],36			"assign":[37			{"v": 0,	"inf": "x"},38			{"v": 1,	"giv": "%22quoted%20text%22"}39			]40		}'41	}42}43var_scope: {44	options = {45		features	: "ASTREL"46	};47	input: {48		function chunkData() { 49			var a = 1;50			var b = 2;51			var x = a + b;52			x = a + b;53		}54	}55	expect: {56		'{57			"query":[58			{"a": 0,	"b": 1,	"f2": ":VarDef[1]-Number"},59			{"a": 2,	"b": 3,	"f2": ":VarDef[1]-Number"},60			{"a": 4,	"b": 0,	"f2": ":VarDef[1]Binary+[0]"},61			{"a": 4,	"b": 2,	"f2": ":VarDef[1]Binary+[1]"},62			{"a": 0,	"b": 2,	"f2": ":Binary+[1]"},63			{"a": 4,	"b": 0,	"f2": ":Assign=[1]Binary+[0]"},64			{"a": 4,	"b": 2,	"f2": ":Assign=[1]Binary+[1]"}65			],66			"assign":[67			{"v": 0,	"inf": "a"},68			{"v": 1,	"giv": "1"},69			{"v": 2,	"inf": "b"},70			{"v": 3,	"giv": "2"},71			{"v": 4,	"inf": "x"}72			]73		}'74	}75}76this_scope: {77	options = {78		features	: "ASTREL"79	};80	input: {81		function chunkData(a) {82			this.x = a;  83		}84		function chunkData2(a) {85			this.x = a;  86		}87	}88	expect: {89		'{90			"query":[91			{"a": 0,	"b": 1,	"f2": ":Assign=[1]"},			92			{"a": 2,	"b": 1,	"f2": "Dot[0]:Assign=[1]"},93			{"a": 0,	"b": 3,	"f2": ":Assign=[1]"},			94			{"a": 2,	"b": 3,	"f2": "Dot[0]:Assign=[1]"}95			],96			"assign":[97			{"v": 0,	"giv": "x"},98			{"v": 1,	"inf": "a"},99			{"v": 2,	"giv": "this"},100			{"v": 3,	"inf": "a"}			101			]102		}'103	}104}105this_is_given: {106	options = {107		features	: "ASTREL"108	};109	input: {110		function chunkData(a) {111			this.x = a;  112		}113	}114	expect: {115		'{116			"query":[117			{"a": 0,	"b": 1,	"f2": ":Assign=[1]"},			118			{"a": 2,	"b": 1,	"f2": "Dot[0]:Assign=[1]"}119			],120			"assign":[121			{"v": 0,	"giv": "x"},122			{"v": 1,	"inf": "a"},123			{"v": 2,	"giv": "this"}124			]125		}'126	}127}128this_attr_scope: {129	options = {130		features	: "ASTREL"131	};132	input: {133		function chunkData(a,b) {134			this.x = a;135			this.x = b;136		}137	}138	expect: {139		'{140			"query":[141			{"a": 0,	"b": 1,	"f2": ":Assign=[1]"},			142			{"a": 2,	"b": 1,	"f2": "Dot[0]:Assign=[1]"},143			{"a": 0,	"b": 3,	"f2": ":Assign=[1]"},144			{"a": 2,	"b": 3,	"f2": "Dot[0]:Assign=[1]"}145			],146			"assign":[147			{"v": 0,	"giv": "x"},148			{"v": 1,	"inf": "a"},149			{"v": 2,	"giv": "this"},150			{"v": 3,	"inf": "b"}151			]152		}'153	}154}155bool_const_type: {156	options = {157		features	: "FNAMES"158	};159	input: {160		function chunkData() { 161			var x = true;162		}163	}164	expect: {165		'{166			"query":[167			{"a": 0,	"b": 1,	"f2": "FNDECL"}			168			],169			"assign":[170			{"v": 0,	"giv": "chunkData"},171			{"v": 1,	"inf": "x"}			172			]173		}'174	}175}176handles_toString_call: {177	options = {178		features	: "FNAMES"179	};180	input: {181		function chunkData() { 182			var x = true;183			x.toString();184		}185	}186	expect: {187		'{188			"query":[189			{"a": 0,	"b": 1,	"f2": "FNDECL"}190			],191			"assign":[192			{"v": 0,	"giv": "chunkData"},193			{"v": 1,	"inf": "x"}			194			]195		}'196	}197}198escape_backslash: {199	options = {200		features	: "FNAMES"201	};202	input: {203		function chunkData(x) { 204			x.replace(/\s/g, "a");205		}206	}207	expect: {208		'{209			"query":[210			{"a": 0,	"b": 1,	"f2": "FNPAR"}			211			],212			"assign":[213			{"v": 0,	"giv": "chunkData"},214			{"v": 1,	"inf": "x"}			215			]216		}'217	}218}219func_no_duplicates: {220	options = {221		features	: "ASTREL"222	};223	input: {224		function chunkData() { 225			var a = new chunkData();226			a = new chunkData();			227		}228	}229	expect: {230		'{231			"query":[232			{"a": 0,	"b": 1,	"f2": ":VarDef[1]New[0]"},233			{"a": 0,	"b": 1,	"f2": ":Assign=[1]New[0]"}234			],235			"assign":[236			{"v": 0,	"inf": "a"},237			{"v": 1,	"giv": "chunkData"}238			]239		}'240	}241}242func_allow_different_features_duplicates: {243	options = {244		features	: "ASTREL"245	};246	input: {247		function chunkData(x) { 248			x.foo(42, 42, 42, 42);249			return 42;250		}251	}252	expect: {253		'{254			"query":[255			{"a": 0,	"b": 1,	"f2": ":Dot[0]"},256			{"a": 0,	"b": 2,	"f2": "Dot[0]:Call[1]-Number"},257			{"a": 0,	"b": 2,	"f2": "Dot[0]:Call[2]-Number"},258			{"a": 0,	"b": 2,	"f2": "Dot[0]:Call[3]-Number"},259			{"a": 0,	"b": 2,	"f2": "Dot[0]:Call[4]-Number"}260			],261			"assign":[262			{"v": 0,	"inf": "x"},263			{"v": 1,	"giv": "foo"},264			{"v": 2,	"giv": "42"}265			]266		}'267	}268}269method_name_fixed: {	270	input: {271		function chunkData(x) { 272			x.foo();273			bar();274		}275	}276	expect: {277		'{278			"query":[279			{"a": 0,	"b": 1,	"f2": ":Dot[0]"},280			{"a": 2,	"b": 0,	"f2": "FNPAR"}281			],282			"assign":[283			{"v": 0,	"inf": "x"},284			{"v": 1,	"giv": "foo"},285			{"v": 2,	"giv": "chunkData"}286			]287		}'288	}...

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