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How to use _initialize_nodes method in lisa

Best Python code snippet using lisa_python

enterprise_backup_merge_tests.py

Source:enterprise_backup_merge_tests.py

...31        if self.reset_restore_cluster:32            self.log.info("*** start to reset cluster")33            self.backup_reset_clusters(self.cluster_to_restore)34            if self.same_cluster:35                self._initialize_nodes(Cluster(),36                                       self.servers[:self.nodes_init])37            else:38                self._initialize_nodes(Cluster(), self.input.clusters[0][39                                                  :self.nodes_init])40            self.log.info("Done reset cluster")41        self.sleep(10)42        """ Add built-in user cbadminbucket to second cluster """43        self.add_built_in_server_user(44            node=self.input.clusters[0][:self.nodes_init][0])45        self.backupset.start = start46        self.backupset.end = end47        self.log.info("*** start restore validation")48        self.backup_restore_validate(compare_uuid=False,49                                     seqno_compare_function=">=",50                                     expected_error=self.expected_error)51    def test_multiple_backups_merge_with_tombstoning(self):52        self.log.info("*** start to load items to all buckets")53        self.expected_error = self.input.param("expected_error", None)54        if self.expires:55            for bucket in self.buckets:56                cb = self._get_python_sdk_client(self.master.ip, bucket)57                for i in range(1, self.num_items + 1):58                    cb.upsert("doc" + str(i), {"key": "value"})59        else:60            self._load_all_buckets(self.master, self.initial_load_gen,61                                   "create", self.expires)62        self.log.info("*** done to load items to all buckets")63        self.backup_create_validate()64        self.backup()65        self.set_meta_purge_interval()66        self._load_all_buckets(self.master, self.delete_gen, "delete",67                               self.expires)68        self.sleep(360, "Sleep for 6 minutes for the meta-data purge "69                        "interval to be completed")70        self.compact_buckets()71        self.backup()72        self.backupset.start = 173        self.backupset.end = len(self.backups)74        self.merge()75        start = self.number_of_backups_taken76        end = self.number_of_backups_taken77        if self.reset_restore_cluster:78            self.log.info("*** start to reset cluster")79            self.backup_reset_clusters(self.cluster_to_restore)80            if self.same_cluster:81                self._initialize_nodes(Cluster(),82                                       self.servers[:self.nodes_init])83            else:84                self._initialize_nodes(Cluster(), self.input.clusters[0][85                                                  :self.nodes_init])86            self.log.info("Done reset cluster")87        self.sleep(10)88        self.backupset.start = start89        self.backupset.end = end90        self.log.info("*** start restore validation")91        self.backup_restore_validate(compare_uuid=False,92                                     seqno_compare_function=">=",...

run_simulation.py

Source:run_simulation.py

...31    connections = np.zeros(shape=(n_nodes,n_nodes))32    for i in range(n_nodes):33        connections[i,i+1:n_nodes] = np.random.rand(n_nodes-i-1)-0.534    return connections35def _initialize_nodes():36    """37    Initializes n nodes with the parameters we care about38    """39    nodes = {}40    nodes['connections'] = _initialize_connections()41    nodes['signals'] = np.zeros((n_nodes,1))42    nodes['profit'] = np.zeros((n_nodes,1))43    return nodes44def _predict(inputs):45    """46    Propagates information from the first to the final node.47    """48    # First, resets all signals.49    nodes['signals'] = inputs50    for n in range(n_nodes):51        # Makes this node a ReLU unit52        nodes['signals'][n,0] = abs(nodes['signals'][n,0]-0.1)53        # Sends this signal to other nodes54        nodes['signals'] += nodes['signals'][n,0]*nodes['connections'][n].reshape(-1,1)55    return nodes['signals']56def _calculate_importances():57    return abs(nodes['connections'])58def _calculate_error_squared(label):59    """60    Calculates the difference between the label and the last node's voltage.61    Assumes the last node's voltage is all we care about when recording the error to the label.62    """63    return np.square(label-nodes['signals'][-1,0])64def _calculate_error_linear(label):65    """66    Calculates the difference between the label and the last node's voltage.67    Assumes the last node's voltage is all we care about when recording the error to the label.68    """69    return label-nodes['signals'][-1,0]70def _calculate_importance_change(recent_reward):71    """72    For a given model, d73    """74def _update_importances(label):75    reward = np.zeros(n_nodes)76    importance_change = np.zeros(n_nodes)77    # Iterates over nodes78    for n in range(n_nodes-1,-1):79        if n==n_nodes-1:80            reward[-1] = _calculate_error_linear(label)81        else:82            print('none')83        # TODO: Calculates the "reward" given to that agent at this timestep as the difference between the desired and the expected outputs for the output.84        # TODO: Calculates the conection update for a given node as the signal that node sent (node signal times connection weight) times the positivity of the effect that signal had on this node.85        # TODO: Draw this as a diagram to make the process clear.86        _calculate_importance_change()87        importance_change[]88    return None89nodes = _initialize_nodes()90sample_input = np.zeros(n_nodes).reshape(-1,1)91prediction = _predict(sample_input)92sample_input_2 = np.ones(n_nodes).reshape(-1,1)93prediction_2 = _predict(sample_input_2)94sample_input_3 = np.zeros(n_nodes).reshape(-1,1)95sample_input_3[0] = 196prediction_3 = _predict(sample_input_3)97# TODO: Calculate the importance of each incoming connection98# TODO: Craft a reward function that propagates importance and updates connection strengths....

072_get_largest_value_of_graph.py

Source:072_get_largest_value_of_graph.py

...26class Graph():27    def __init__(self,chars,edges=[]):28        self.chars=chars29        self.edges=edges30        self._initialize_nodes(chars)31        self._initialize_edges(edges)32    def _initialize_nodes(self,chars):33        self.nodes=[ Node(c) for c in chars ]34    def _initialize_edges(self,edges):35        for edge in edges:36            start,end=edge37            node=self.nodes[start]38            node.edges.append(edge)39    def __repr__(self):40        return "nodes: {}, edges: {}".format(self.nodes,self.edges)41    def _get_connected_nodes(self, node):42        return [ self.nodes[end] for start,end in node.edges ]43    def _get_path_value(self,path):44        pathvals=defaultdict(int)45        for p in path:46            pathvals[p]+=1...

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