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How to use __pop method in hypothesis

Best Python code snippet using hypothesis

2kyu_befunge_interpreter.py

Source:2kyu_befunge_interpreter.py

...106                col = 0107            else:108                self.matrix[row][col] = c109                col += 1110    def __pop(self):111        try:112            res = self.stack.pop()113        except:114            res = 0115        return res116    def __push(self, s):117        self.stack.append(s)118    def __direction(self, direction):119        self.current_direction = direction120    def __push_num(self):121        self.__push(int(self.matrix[self.current_row][self.current_col]))122    def __add(self):123        x = self.__pop()124        y = self.__pop()125        self.__push(x + y)126    def __subtract(self):127        x = self.__pop()128        y = self.__pop()129        self.__push(y - x)130    def __multiple(self):131        x = self.__pop()132        y = self.__pop()133        self.__push(x * y)134    def __divide(self):135        x = self.__pop()136        y = self.__pop()137        if y == 0:138            self.__push(0)139        else:140            self.__push(round(y / x, 0))141    def __mod(self):142        x = self.__pop()143        y = self.__pop()144        if x == 0:145            self.__push(0)146        else:147            self.__push(y % x)148    def __not(self):149        x = self.__pop()150        if x == 0:151            self.__push(1)152        else:153            self.__push(0)154    def __gt(self):155        x = self.__pop()156        y = self.__pop()157        if y > x:158            self.__push(1)159        else:160            self.__push(0)161    def __moving_right(self):162        self.__direction('>')163    def __moving_left(self):164        self.__direction('<')165    def __moving_up(self):166        self.__direction('^')167    def __moving_down(self):168        self.__direction('v')169    def __moving_random_direction(self):170        direction = ['>', '<', '^', 'v']171        self.__direction(direction)172        self.BEFUNGE_CODE_TABLE[random.choice(direction)]()173    def __moving_by_cond_right_left(self):174        x = self.__pop()175        if x == 0:176            self.BEFUNGE_CODE_TABLE['>']()177        else:178            self.BEFUNGE_CODE_TABLE['<']()179    def __moving_by_cond_up_down(self):180        x = self.__pop()181        if x == 0:182            self.BEFUNGE_CODE_TABLE['v']()183        else:184            self.BEFUNGE_CODE_TABLE['^']()185    def __string_mode(self):186        self.__moving()187        n = ord(str(self.matrix[self.current_row][self.current_col]))188        while n != 34:189            self.__push(n)190            self.__moving()191            n = ord(str(self.matrix[self.current_row][self.current_col]))192    def __duplicate(self):193        x = self.__pop()194        self.__push(x)195        self.__push(x)196    def __swap(self):197        x = self.__pop()198        y = self.__pop()199        self.__push(x)200        self.__push(y)201    def __discard(self):202        self.__pop()203    def __end(self):204        self.is_ended = True205    def __get_integer(self):206        self.result.append(str(self.__pop()))207    def __get_char(self):208        self.result.append(chr(self.__pop()))209    def __trampoline(self):210        self.__moving()211    def __put_to_xy(self):212        x = self.__pop()213        y = self.__pop()214        v = self.__pop()215        self.matrix[x][y] = chr(v)216    def __get_from_xy(self):217        y = self.__pop()218        x = self.__pop()219        self.__push(ord(self.matrix[y][x]))220    def __nothing(self):221        pass222    def __moving(self, count=1):223        if self.current_direction == '>':224            self.current_col += count225        elif self.current_direction == '<':226            self.current_col -= count227        elif self.current_direction == '^':228            self.current_row -= count229        elif self.current_direction == 'v':230            self.current_row += count231class TestBefunge(unittest.TestCase):232    def test_befunge_is_valid(self):...

Mopso.py

Source:Mopso.py

1# encoding: utf-823import time4import numpy as np5from util import update, drawCurve, testFunc, NDsort6import memory_profiler as mem78class Mopso:9    def __init__(self, num_pop, max_, min_, threshold, grid_div=10):10        self.__grid = grid_div11        self.__num_pop = num_pop  # ç§ç¾¤ç²åæ°é12        self.__threshold = threshold13        self.__max = max_14        self.__min = min_1516        # self.max_v = (max_-min_)*0.5  #éåº¦ä¸é17        # self.min_v = (max_-min_)*(-1)*0.5 #éåº¦ä¸é18        self.__max_v = 100 * np.ones(len(max_), )  # éåº¦ä¸ä¸é,éåº¦ä¸åå¨ä¸ä¸éï¼å æ¤è®¾ç½®å¾å¤§19        self.__min_v = -100 * np.ones(len(min_), )2021        self.__pop = None22        self.__v = None23        self.__fit = None24        self.__pop_pbest = None25        self.__fit_pbest = None26        self.__pop_gbest = None27        self.__fit_gbest = None28        self.__archive_pop = None29        self.__archive_fit = None3031        self.__draw = drawCurve.DrawParetoCurve()3233    def evaluation_fitness(self):34        self.__fit = testFunc.obj_func("value", "DTLZ2", 2, self.__pop)3536    def initialize(self):37        """38        åå§åç²åç¾¤39        :return:40        """41        # åå§åç²åä½ç½® pop[i]42        # self.__num_pop ç²åç¾¤æ°é43        self.__pop = self.__init_loc(self.__num_pop, self.__max, self.__min)44        # åå§åç²åéåº¦ å¨045        self.__v = self.__init_v(self.__num_pop, self.__max_v, self.__min_v)46        # è®¡ç®éåºåº¦47        self.evaluation_fitness()48        # åå§åä¸ªä½æä¼49        # TODO ç¬¬äºä¸ªåæ°åºè¯¥æ¯éåº¦ï¼50        self.__pop_pbest, self.__fit_pbest = self.__init_pbest(self.__pop, self.__fit)51        # åå§åå¤é¨åæ¡£52        self.__archive_pop, self.__archive_fit = self.__init_archive(self.__pop, self.__fit)53        # åå§åå¨å±æä¼54        self.__pop_gbest, self.__fit_gbest = update.update_gbest(self.__archive_pop, self.__archive_fit, self.__grid,55                                                                 self.__num_pop)5657    def update(self):58        """59        æ´æ°ç²åéåº¦ãä½ç½®ãéåºåº¦ãä¸ªä½æä¼ãå¤é¨åæ¡£ãå¨å±æä¼60        :return:61        """62        self.__v = update.update_v(self.__v, self.__min_v, self.__max_v, self.__pop, self.__pop_pbest, self.__pop_gbest)63        self.__pop = update.update_pop(self.__pop, self.__v, self.__min, self.__max)6465        self.evaluation_fitness()6667        self.__pop_pbest, self.__fit_pbest = update.update_pbest(self.__pop, self.__fit, self.__pop_pbest,68                                                                 self.__fit_pbest)6970        self.__archive_pop, self.__archive_fit = update.update_archive(self.__pop, self.__fit, self.__archive_pop,71                                                                       self.__archive_fit,72                                                                       self.__threshold, self.__grid)7374        self.__pop_gbest, self.__fit_gbest = update.update_gbest(self.__archive_pop, self.__archive_fit, self.__grid,75                                                                 self.__num_pop)7677    def run(self, iterate):78        # å¼å§æ¶é´æ³79        beg = time.time()80        print(f'è¿è¡åå ç¨ååï¼{mem.memory_usage()}')81        self.initialize()82        self.__draw.show(self.__pop, self.__fit, self.__archive_pop, self.__archive_fit, -1)83        for i in range(iterate):84            self.update()  # æ´æ°ç§ç¾¤85            # numpy.round() è¿åæµ®ç¹æ°çåèäºå¥å¼86            print('ç¬¬', i+1, 'ä»£å·²å®æï¼èè´¹æ¶é´: ', np.round(time.time() - beg, 2), "s")87            self.__draw.show(self.__pop, self.__fit, self.__archive_pop, self.__archive_fit, i)88        print(f'è¿è¡åå ç¨ååï¼{mem.memory_usage()}')89        return self.__archive_pop, self.__archive_fit9091    @staticmethod92    def __init_loc(pop, pop_max, pop_min):93        # åå§åç²åä½ç½®94        dim = len(pop_max)  # è¾å¥åæ°ç»´åº¦95        # numpy.random.uniform(low, high, size) ä»åååå¸ä¸éæºéæ ·ï¼sizeæ¯è¾åºæ ·æ¬æ°é96        return np.random.uniform(0, 1, (pop, dim)) * (pop_max - pop_min) + pop_min9798    @staticmethod99    def __init_v(pop, v_max, v_min):100        v_dim = len(v_max)  # ç»´åº¦101        # v_ = np.random.uniform(0,1,(particals,v_dim))*(v_max-v_min)+v_min102        return np.zeros((pop, v_dim))103104    @staticmethod105    def __init_pbest(pop, fit):106        return pop, fit107108    @staticmethod109    def __init_archive(pop, fit):110        # NDSort()[0] ååºç¬¬ä¸ç»è¿åå¼ï¼å¦ææ¯1åæ¯ç¬¬ä¸å±çç²å111        front_index = NDsort.NDSort(fit, pop.shape[0])[0] == 1112        front_index = np.reshape(front_index, (-1,)) # ç©éµè½¬æè¡åé113114        # curr_archiving_in = pop[front_index]115        # curr_archiving_fit = fit[front_index]116117        # pareto_c = pareto.Pareto_(pop,fitness_)118        # curr_archiving_in_,curr_archiving_fit_ = pareto_c.pareto()
...

bssa.py

Source:bssa.py

1from salp import Salp2from accuracy import cal_cost_knn, cal_cost_svm3import numpy as np4import logging 5import os6from copy import deepcopy7logger = logging.getLogger("main.bssa")8class BSSA:9    10    def __init__(self, pop, dim, tf, ub, lb, upate_strategy):11        self.__pop = [Salp(dim, ub, lb) for i in range(pop)]12        self.__tf = tf13        self.__ub = ub14        self.__lb = lb15        self.__selected_features_history = []16        self.__cost_history = []17        self.__acc_history = []18        self.__update_strategy = upate_strategy19        self.__cost_func = cal_cost_svm20    21    def get_pop(self):22        return self.__pop23    def set_pop(self, pop):24        self.__pop = pop25    def get_best_salp(self):26        return self.__pop[0]27    def get_best_position(self):28        return self.__pop[0].get_position()29    def get_best_selected(self):30        return np.round(self.get_best_position())31    def get_best_cost(self):32        return self.__pop[0].get_cost()33    def replace_with_worst_salp(self, best_salp):34        self.__pop = sorted(self.__pop, key=lambda x:x.get_cost())35        self.__pop[-1].set_position(best_salp.get_position())36        self.__pop[-1].set_cost(best_salp.get_cost())37        38    def reset_history(self):39        self.__cost_history = []40        self.__selected_features_history = []41        self.__acc_history = []42    def get_cost_history(self):43        return self.__cost_history44    def get_selected_features_history(self):45        return self.__selected_features_history46    def get_acc_history(self):47        return self.__acc_history48    def train(self, max_iteration, train_data, train_target, test_data, test_target):49        us = self.__update_strategy50        tf = self.__tf51        cf = self.__cost_func52        #measure accuracy and sort respect to the fitness53        for s in self.__pop:54            c, _, _ = cf(s.get_position(), train_data, train_target)55            s.set_cost(c)56        self.__pop = sorted(self.__pop, key=lambda x:x.get_cost())57        for s in self.__pop:58            logger.debug("cost is {}".format(s.get_cost()))59        #select the best one as food60        food = self.__pop[0]61        #it should be sorted62        for i in range(max_iteration):63            c1 = us(i, max_iteration)64            for this_s, pre_s, j in zip(self.__pop[1:], 65                                        self.__pop[-1:] + self.__pop[1:-1], 66                                        range(len(self.__pop))):67                if j<= len(self.__pop)/2.0:68                    c2 = np.random.rand(this_s.get_dim())69                    c3 = np.random.rand(this_s.get_dim())70                    c3 = np.array(list(map(lambda x:-1 if x<0.5 else 1, c3)))71                    this_s.set_position(food.get_position() + c1*c2*c3)72                else:73                    this_s.set_position((this_s.get_position() + pre_s.get_position())/2)74            for s in self.__pop:75                u = self.__ub76                l = self.__lb77                pos = s.get_position()78                pos = np.array(list(map(lambda x:l if x<l else(u if x>u else x), pos)))79                s.set_position(pos)80                c, _, _ = cf(s.get_position(), train_data, train_target)81                s.set_cost(c)82                if c < food.get_cost():83                    food.set_position(s.get_position())84                    food.set_cost(s.get_cost())85                    logger.debug("best changed to this cost {} last cost {}".format(s.get_cost(), food.get_cost()))   86            87            _, e, f = cf(food.get_position(), train_data, train_target)88            self.__acc_history.append(1 - e)89            self.__selected_features_history.append(f)90            self.__cost_history.append(food.get_cost())91            logger.info("iter {} cost {} pid {} ppid {}".format(i, food.get_cost(), os.getpid(), os.getppid()))...

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