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How to use _representation method in autotest

Best Python code snippet using autotest_python

ant.py

Source:ant.py

1from random import random2import numpy3from copy import deepcopy4from texttable import Texttable5class Ant:6    def __init__(self, size):7        self._size = size8        self._representation = [[] for i in range(size * 2)]9        self._graph = [self._representation]10        self._freeSpots = size - 111    def getFreeSpotsCount(self):12        return self._freeSpots13    def getRepresentation(self):14        return self._representation15    def getGraph(self):16        return self._graph[:]17    def setRepresentation(self, newRepresentation):18        if len(self._representation[-1]) > len(newRepresentation[-1]):19            self.decreaseFreeSpots()20        self._representation = deepcopy(newRepresentation)21    def decreaseFreeSpots(self):22        self._freeSpots -= 123    def nextPossibilities(self):24        possibilities = []25        for i in range(self._size * 2 * (self._freeSpots)):26            newPossibility = deepcopy(self._representation)27            for row in range(self._size * 2):28                possibleNumbers = [i for i in range(1, self._size + 1)]29                for elem in self._representation[row]:30                    possibleNumbers.remove(elem)31                # if row >= self._size and newPossibility[row - self._size][-1] in possibleNumbers:32                #     possibleNumbers.remove(newPossibility[row - self._size][-1])33                choice = numpy.random.choice(possibleNumbers)34                newPossibility[row].append(choice)35                possibleNumbers.remove(choice)36            possibilities.append(newPossibility)37        return possibilities38    def move(self, q0, trace, alpha, beta):39        nextPossibilities = self.nextPossibilities()40        distances = []41        if len(nextPossibilities) == 0:42            return False43        auxAnt = Ant(self._size)44        for position in nextPossibilities:45            auxAnt.setRepresentation(position)46            distances.append([position, auxAnt.fitness() - self.fitness()])47        for i in range(len(distances)):48            index = [0, False]49            while index[0] < len(trace) or index[1]:50                if trace[index[0]] == distances[i][0]:51                    index[1] = True52                index[0] += 153            if index[1]:54                distances[i][1] = (distances[i][1] ** beta) * (trace(index[0]) ** alpha)55        if numpy.random.random() < q0:56            distances = min(distances, key=lambda elem:elem[1])57            self.setRepresentation(distances[0])58            self._graph.append(self._representation)59        else:60            suma = 061            for elem in distances:62                suma += elem[1]63            if suma == 0:64                choice = numpy.random.randint(0, len(distances))65                self.setRepresentation(distances[choice][0])66                self._graph.append(self._representation)67                return68            distances = [[distances[i][0], distances[i][1] / suma] for i in range(len(distances))]69            for i in range(len(distances)):70                sum = 071                for j in range(i+1):72                    sum += distances[j][1]73                distances[i][1] = sum74            choice = numpy.random.random()75            i = 076            while choice > distances[i][1]:77                i += 178            self.setRepresentation(distances[i][0])79            self._graph.append(self._representation)80        return True81    def __str__(self):82        table = Texttable()83        for i in range(self._size):84           row = []85           for j in range(len(self._representation[i])):86              row.append((self._representation[i][j], self._representation[i + self._size][j]))87           table.add_row(row)88        return table.draw()89    def fitness(self):90        fitness = 091        for i in range(self._size):92            for j in range(len(self._representation[i])):93                if self._representation[i][j] == self._representation[i + self._size][j]:94                    fitness += 195                if i < len(self._representation[i]) and self._representation[j][i] == self._representation[j + self._size][i]:96                    fitness += 197        for i in range(self._size - 1):98            for j in range(i + 1, self._size):99                fitness += numpy.count_nonzero(100                    numpy.equal(self._representation[i + self._size], self._representation[j + self._size]))101                fitness += numpy.count_nonzero(numpy.equal(self._representation[i], self._representation[j]))102        for i in range(len(self._representation[-1]) - 1):103            column11 = [self._representation[j][i] for j in range(self._size)]104            column12 = [self._representation[j + self._size][i] for j in range(self._size)]105            for j in range(i + 1, len(self._representation[i])):106                column21 = [self._representation[k][j] for k in range(self._size)]107                column22 = [self._representation[k + self._size][j] for k in range(self._size)]108                fitness += numpy.count_nonzero(numpy.equal(column11, column21))109                fitness += numpy.count_nonzero(numpy.equal(column12, column22))...

containers.py

Source:containers.py

1from typing import Iterable, Any2from .errors import StackEmptyException, QueueEmptyException3__all__ = [4  "Queue", "Stack"5]6def foreach(function, iterable: Iterable):7    for element in iterable:8        function(element)9class Queue(list):10  def __init__(self, _: Iterable = []):11    super(Queue, self).__init__(_)12    self._representation = []13  def __getattribute__(self, name):14    if name in ['append', 'index', 'remove']:15      raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")16    return super(list, self).__getattribute__(name)17  def foreach(self, function):18    foreach(function, self)19  def enqueue(self, item: Any):20    """Add an item to the beginning of the queue."""21    self.insert(0, item)22    self._representation.insert(0, item)23    return self24  def dequeue(self):25    """Removes the topmost item in the queue and returns it."""26    if(len(self) == 0 or len(self._representation) == 0):27      raise QueueEmptyException28    self._representation.pop(len(self) - 1)29    return self.pop(len(self) - 1)30  def peek(self):31    """Returns the topmost item in the queue without removing it."""32    if(len(self) == 0):33      raise QueueEmptyException34    return self[len(self) - 1]35  @property36  def empty(self):37    """A boolean property indicating whether the queue is empty or not."""38    return len(self) == 039  def __iter__(self):40    for i in self.__reversed__():41      yield i42  def extend(self, iterable: Iterable):43    for i in iterable:44      self.enqueue(i)45  def __str__(self):46    return f"<Queue {self._representation}>"47  @classmethod48  def from_dict(cls):49    pass50class Stack(list):51  def __init__(self, _: Iterable = []):52    super(Stack, self).__init__(_)53    self._representation = _54  def __getattribute__(self, name):55    if name in ['append', 'index', 'remove']:56      raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")57    return super(list, self).__getattribute__(name)58  def foreach(self, function):59    foreach(function, self)60  def push(self, item: Any):61    """Add an item to the end of the stack."""62    self.insert(len(self), item)63    self._representation.append(item)64    return self65  def pop(self):66    """Removes the last added item in the list and returns it."""67    if(len(self) == 0 or len(self._representation) == 0):68      raise StackEmptyException69    self._representation.pop(len(self) - 1)70    return self.pop(len(self) - 1)71  def peek(self):72    """Returns the topmost item in the stack without removing it."""73    if(len(self) == 0):74      raise StackEmptyException75    return self[len(self) - 1]76  @property77  def empty(self):78    """A boolean property indicating whether the stack is empty or not."""79    return len(self) == 080  def search(self, o: Any) -> int:81    """Returns the 1-based position where an object is on this stack. If the object o occurs as an item in this stack, this method returns the distance from the top of the stack of the occurrence nearest the top of the stack; the topmost item on the stack is considered to be at distance 1. The equals method is used to compare o to the items in this stack."""82    index = 083    for i in self.__reversed__():84      index += 185      if o == i:86        return index87    return -188  def __iter__(self):89    for i in self.__reversed__():90      yield i91  def __str__(self):92    return f"<Stack {self._representation}>"93  def extend(self, iterable: Iterable):94    for i in iterable:...

representers.py

Source:representers.py

1import numpy as np2from gep_utils import *3class CheetahRepresenter():4    def __init__(self):5        self._description = ['mean_vx', 'min_z']6        # define goal space7        self._initial_space = np.array([[-4, 7],[-3,2]])8        self._representation = None9    def represent(self, obs_seq, act_seq=None):10        obs = np.copy(obs_seq)11        mean_vx = np.array([obs[0, 8, :].mean()])12        min_z = np.array([obs[0, 0, :].min()])13        self._representation = np.concatenate([mean_vx, min_z], axis=0)14        # scale representation to [-1,1]^N15        self._representation = scale_vec(self._representation, self._initial_space)16        self._representation.reshape(1, -1)17        return self._representation18    @property19    def initial_space(self):20        return self._initial_space21    @property22    def dim(self):23        return self._initial_space.shape[0]24class CMCRepresenter():25    def __init__(self):26        self._description = ['max position', 'range position', 'spent energy']27        # define goal space28        self._initial_space= np.array([[-0.6, 0.6], [0., 1.8], [0, 100]])  # space in which goal are sampled29        self._representation = None30    def represent(self, obs_seq, act_seq):31        spent_energy = np.array([np.sum(act_seq[0, 0, np.argwhere(~np.isnan(act_seq))] ** 2 * 0.1)])32        diff = np.array([np.nanmax(obs_seq[0, 0, :]) - np.nanmin(obs_seq[0, 0, :])])33        max = np.array([np.nanmax(obs_seq[0, 0, :])])34        self._representation = np.concatenate([max, diff, spent_energy], axis=0)35        # scale representation to [-1,1]^N36        self._representation = scale_vec(self._representation, self._initial_space)37        self._representation.reshape(1, -1)38        return self._representation39    @property40    def initial_space(self):41        return self._initial_space42    @property43    def dim(self):...

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