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How to use _valid_connection method in autotest
Best Python code snippet using autotest_python
cgp_py3.py
Source:cgp_py3.py 
1"""2Cartesian Genetic Programming3=============================4Genetic Programming is concerned with the automatic evolution (as in 5Darwinian evolution) of computational structures (such as mathematical 6equations, computer programs, digital circuits, etc.). CGP is a highly 7efficient and flexible form of Genetic Programming that encodes a graph 8representation of a computer program. It was developed by Julian Miller9in 1999. For more information see: http://cartesiangp.co.uk/10This script written by Petr Dvoracek in 2017. http://flea.name/11"""12import random 13from copy import deepcopy14# The main pillar of this algorithm lies in the directed oriented graph in15# which each node represents a specific operation. 16# The nodes are commonly arranged in square lattice.17DEFAULT_MATRIX_SIZE = (2, 5) # ROWS, COLS18# The interconnection between columns. 19DEFAULT_LEVEL_BACK = 5 # If the value is equal to number of columns then 20 # the interconnection is maximal.21# The operation set. 22# List of tuples(`str` name, `int` arity (inputs), `function` operation). 23DEFAULT_OPERATION_SET = set([("BUF", 1, lambda *arg: arg[0]),24 ("NOT", 1, lambda *arg: ~arg[0]), 25 ("AND", 2, lambda *arg: ~(arg[0] & arg[1])), 26 ("OR", 2, lambda *arg: ~(arg[0] | arg[1])), 27 ("XOR", 2, lambda *arg: arg[0] & arg[1]), 28 ("NAND", 2, lambda *arg: arg[0] | arg[1]),29 ("NOR", 2, lambda *arg: arg[0] ^ arg[1]), 30 ("XNOR", 2, lambda *arg: ~(arg[0] ^ arg[1])),31 ])32# Lets find full adder by default.33# For all input combinations define:34DEFAULT_INPUT_DATA = [0b00001111, # A35 0b00110011, # B36 0b01010101] # Carry in37# Define output combinations38DEFAULT_TRAINING_DATA = [0b01101001, # SUM39 0b00010111] # Carry out40# The CGP is a genetic algorithm. Therefore, we need to define evolutionary parameters.41# The number of candidate solutions.42DEFAULT_POP_SIZE = 5 43# Maximal generations (how long the algorithm will run)44DEFAULT_MAX_GENERATIONS = 10000 45# This function returns the list of objects with poistion `index` in the `nested_list` (list of arrays).46# Example: get_sublist(1, [("A", 3, 5), ("C", 1), ("D", "C")]) --> [3, 1, "C"]47get_sublist = lambda index, nested_list : list(map(lambda sublist: sublist[index], nested_list))48# How many genes will be mutated.49# 10% of the chromosome length can be mutated. Chromosome length = each_node * len(node) + len(last_node); The length of node is maximal arity + the operation.50DEFAULT_MUTATIONS = int((DEFAULT_MATRIX_SIZE[0] * DEFAULT_MATRIX_SIZE[1] * (max(get_sublist(1, DEFAULT_OPERATION_SET)) + 1) + len(DEFAULT_TRAINING_DATA)) * 0.10)51#DEFAULT_MUTATIONS = 452class CGP(object):53 # Printing if we find better fitness during the evolution.54 PRINT_INFO = False55 def __set_connections(self):56 """ Calculates the set of valid connections """57 # For each column create valid connection interval from previous `l-back` connections.58 self._valid_connection = [(self.rows * (column - self.level_back), self.rows * column) for column in range(self.columns)] 59 # Filter invalid indexes which are negative -> make them 060 self._valid_connection = list(map(lambda sublist: list(map(lambda x: x if (x >= 0) else 0, sublist)), self._valid_connection))61 def __test_operation_set(self, operation_set):62 """ Check the validity of operation set.63 64 Parameters65 operation_set : list of operations66 Each opeartaion is a tuple(`str` name, `int` arity, `function` operation). 67 Raises68 CGPError69 If the operation set contains inconsistent operations.70 """71 72 # Test for empty set.73 if not operation_set:74 raise CGPError("EMPTY_SET")75 # Test if each operation has a triplet of (str, int, function) and tests the arity.76 for operation in operation_set:77 if len(operation) != 3 or type(operation[0]) is not str \78 or type(operation[1]) is not int \79 or not callable(operation[2]):80 raise CGPError("INCONSISTENT_SET", value=operation)81 # Negative arity of function is stupid. Unless, you are doing some deep shit math. 82 if operation[1] < 0: 83 raise CGPError("INCONSISTENT_ARITY", value=operation)84 def set_data(self, input_data, training_data):85 """ Set Input and output data86 87 Parameters88 input_data : list89 The list of input data. In the case of digital circuit design, 90 this contains all the input combinations. 91 training_data : list92 The target circuit.93 """94 self.input_data_range = list(range(-len(input_data), 0))95 self.input_data = input_data96 self.training_data = training_data97 def set_matrix(self, matrix, level_back=0):98 """ Initate the matrix. 99 Parameters100 matrix : tuple (int, int)101 This tuple represents the size of matrix (rows, columns)102 Other Parameters103 level_back : int104 The interconnection between columns.105 """106 self.matrix = matrix # Though, it is not used.107 self.rows = matrix[0]108 self.columns = matrix[1]109 self.level_back = level_back if level_back > 0 else matrix[1] # If `level_back` has invalid value (0 or less), then the interconnection is maximal. 110 self.__set_connections()111 112 def set_operation_set(self, operation_set):113 """ Set the operation set.114 Parameters115 operation_set : list of operations116 Each opeartaion is a tuple(`str` name, `int` arity, `function` operation). 117 Raises118 CGPError119 If the operation set contains inconsistent operations.120 """121 self.__test_operation_set(operation_set)122 self.operation_set = operation_set123 # Transpose the operation set data.124 self.operations = get_sublist(2, operation_set)125 self.operations_arity = get_sublist(1, operation_set)126 self.operations_names = get_sublist(0, operation_set)127 # Get maximal arity128 self.maximal_arity = max(self.operations_arity)129 130 def __init__(self, matrix, operation_set, input_data, training_data, level_back=0):131 """ Initate the matrix, node arrangement, operation set and i/o data. 132 Parameters133 matrix : tuple (int, int)134 This tuple represents the size of matrix (rows, columns)135 operation set : list of tuples (`str` name, `int` arity, `function` operation). 136 The operation set.137 input_data : list138 The list of input data. In the case of digital circuit design, 139 this contains all the input combinations. 140 training_data : list141 The target circuit.142 Other Parameters143 level_back : int144 The interconnection between columns.145 Raises146 CGPError147 If the operation set contains inconsistent operations.148 """149 super(CGP, self).__init__()150 151 self.set_matrix(matrix, level_back=level_back)152 self.set_operation_set(operation_set)153 self.set_data(input_data, training_data)154 def __create_chromosome(self):155 """ Initate the chromosome. 156 The chromsome encodes the interconnection of the CGP graph. See: http://www.oranchak.com/cgp/doc/fig2.jpg157 """158 # Init array for chromosome159 chromosome = []160 # This function selects random value from input data or previous node in the graph.161 connect = lambda connection: random.choice(self.input_data_range + list(range(connection[0], connection[1])))162 163 # Nodes may differ by their connection between columns. The interconnection is depended on the value of level back. 164 for column_index in range(self.columns):165 # Get valid connection range for nodes in the column166 valid_con = self._valid_connection[column_index]167 # Function for the creation of the node. Firstly creates the connections, then adds the node operation. 168 create_node = lambda: [connect(valid_con) for _ in range(self.maximal_arity)] + [random.choice(self.operations)]169 chromosome += [create_node() for _ in range(self.rows)]170 # Add last `node` which binds the primary outputs of the graph to nodes.171 valid_con = (0, len(chromosome))172 chromosome += [[connect(valid_con) for _ in self.training_data]]173 return chromosome174 def __init_pop(self, pop_size):175 """ Initiate the population of `pop_size` size """176 self.pop = [self.__create_chromosome() for _ in range(pop_size)]177 # The best individual is saved in `self.best_chrom` with the best fitness `self.fitness`.178 self.best_chrom = deepcopy(self.pop[0])179 self.best_fitness = self.max_fitness180 def __mutate_gene(self, chromosome):181 """ Mutate random value in a `chromsome`. 182 183 Parameters184 chromosome : chromsome strucutre185 The representation of candidate solution which is subject to a mutation. 186 """187 # This function selects random value from input data or previous node in the graph.188 connect = lambda connection: random.choice(self.input_data_range + list(range(connection[0], connection[1])))189 # Select some node190 random_node_idx = random.randint(0, len(chromosome) - 1)191 random_node = chromosome[random_node_idx]192 193 # And select an index from the node194 random_idx = random.randint(0, len(random_node) - 1) # Note: A <= rnd <= B195 previous_value = random_node[random_idx]196 if random_node is chromosome[-1]:197 # We mutate the last node198 while previous_value == random_node[random_idx]:199 random_node[random_idx] = connect((0, len(chromosome)))200 elif random_idx == self.maximal_arity:201 # We mutate the operation (indexing from 0)202 while previous_value == random_node[random_idx]:203 random_node[random_idx] = random.choice(self.operations)204 else:205 # We mutate the connection206 while previous_value == random_node[random_idx]:207 random_node[random_idx] = connect(self._valid_connection[ random_node_idx // self.rows ])208 def mutate(self):209 """ Creates a copy of the best solution and mutates it random-times.210 Return211 chromsome : chromosome structure212 The representation of the other candidate solution, which is similar to the best found solution.213 """214 # Copy the best chromsome215 chromosome = deepcopy(self.best_chrom)216 # And mutate it as much as possible217 for _ in range(random.randint(1, self.mutation_rate+1)): # Mutate it at least once. Else the chromsome wouldn't change.218 self.__mutate_gene(chromosome)219 return chromosome220 def init_eval(self):221 """ This function is started before the run of evolution. """222 # Buffer for the evaluation.223 buffer_data = deepcopy(self.input_data) + list(range(self.rows * self.columns + len(self.training_data)))224 buffer_indexes = list(range(-len(self.input_data), len(buffer_data) - len(self.input_data)))225 self.buffer = dict(zip(buffer_indexes, buffer_data))226 227 # Maximal fitness228 self.max_fitness = len(self.training_data) * (2 ** len(self.input_data))229 # Fitness mask.230 # V mask V The number of bites V # We are doing parallel evaluation. 231 self.bitmask = 2 ** (2 ** len(self.input_data)) - 1232 # Set generations.233 self.generation = 0234 def chrom_to_str(self, chromosome):235 """ Creates a string representation of chromosome, in which the nodes are aligned into a lattice. 236 Each node has syntax `index` : [`previous index`, ... , `previous index`, `Function name`] 237 If the node was primary output, then the node is ended with a string `<- `. 238 The primary outputs are also printed in the last line for the sake of their permutation. 239 Parameters240 chromsome : chromosome structure241 The representation of chromosome.242 Returns243 str244 The string representation of chromosome.245 """246 # Some math stuff247 max_input_strlen = len(str(self.columns * self.rows))248 max_length_of_operation_string = max(list(map(len, self.operations_names)))249 chrom_str = ""250 for i in range(self.rows):251 line = ""252 for j in range(self.columns):253 # Grab the node index 254 node_idx = j * self.rows + i255 node = chromosome[node_idx]256 # Find the inputs, and align them.257 inputs = node[:-1] # The last node is operation; the rest are the inputs.258 aligned_inputs = list(map(lambda x: str(x).rjust(max_input_strlen), inputs))259 # Find out the operation name and align it.260 operation = node[-1]261 operation_index = self.operations.index(operation)262 operation_name = self.operations_names[operation_index]263 aligned_operation_name = operation_name.rjust(max_length_of_operation_string)264 # save it265 if node_idx in chromosome[-1]:266 flag = "<- "267 else:268 flag = " "269 line += str(node_idx).rjust(max_input_strlen) + ": [" + ", ".join(aligned_inputs) + ", " + aligned_operation_name + "]" + flag270 chrom_str += line + "\n"271 return chrom_str + str(chromosome[-1])272 273 def eval(self, chromosome):274 """ Evaluates the `chromosome` and returns the fitness value. Also checks if we found a better solution.275 Parameters276 chromsome : chromosome structure277 The representation of a candidate solution.278 Returns279 int : fitness value280 The quality of the candidate solution.281 TODO282 Optimise it. Skip neutral mutations. Skip unused nodes. (Maybe use C/C++ function?)283 Move the fitness check 284 """285 fitness = 0 286 # Evaluate each node.287 for idx, node in enumerate(chromosome[:-1]):288 # TODO SKIP unused nodes289 # Save the value of the operation `node[-1]`. The arguments are given in `node[:-1]` and they are the indexes into the `buffer`.290 self.buffer[idx] = node[-1](*[self.buffer[i] for i in node[:-1]])291 # Grab the value 292 for idx, target in zip(chromosome[-1], self.training_data):293 #print idx, target, self.bitmask294 fitness += bin((self.buffer[idx] ^ target) & self.bitmask).count("1")295 # Checks if we found a better solution.296 # Shouldnt be here.297 if fitness <= self.best_fitness:298 if CGP.PRINT_INFO and fitness < self.best_fitness:299 print("Generation: " + str(self.generation).rjust(10) + "\tFitness: " + str(fitness))300 self.best_fitness = fitness301 self.best_chrom = deepcopy(chromosome)302 return fitness303 def run(self, pop_size, mutations, generations):304 """ Runs the evolution.305 Parameters306 chromsome : chromosome structure307 The representation of chromosome.308 """309 # Init evaluation310 self.init_eval()311 # Init mutation312 self.mutation_rate = mutations313 # Creates first population `self.pop` and evaluates it `self.eval`.314 self.__init_pop(pop_size)315 # Run evolution316 for self.generation in range(generations):317 # Evaluate pop318 list(map(self.eval, self.pop))319 # Mutate pop; fix the peroformance 320 new_pop = [self.mutate() for _ in self.pop]321 self.pop = new_pop322 #break323 324 def __str__(self):325 return self.chrom_to_str(self.best_chrom)326 327class CGPError(Exception):328 def __init__(self, error_code, value=""):329 """ Inits the exception. """330 self.error_code = error_code331 self.value = value332 def __str__(self):333 """ Print error message, depending on the given error code. """334 error_msg = {335 "EMPTY_SET" : "The operation set is empty.",336 "INCONSISTENT_SET" : "The operation set contains inconsistent operation: " + repr(self.value) + "\n"\337 + "Please use triplet (`str` name, `int` arity, `function` operation)",338 "INCONSISTENT_ARITY" : "The arity of operation " + repr(self.value) +" can not be negative.", # Note: if you are doing some high math sh.t, then write your own CGP with blackjack and h..kers.339 }.get(self.error_code, "Unknown error - " + repr(self.error_code))340 return error_msg341if __name__ == '__main__':342 CGP.PRINT_INFO = True343 cgp = CGP(DEFAULT_MATRIX_SIZE, DEFAULT_OPERATION_SET, DEFAULT_INPUT_DATA, DEFAULT_TRAINING_DATA)344 cgp.run(DEFAULT_POP_SIZE, DEFAULT_MUTATIONS, DEFAULT_MAX_GENERATIONS)...
cgp.py
Source:cgp.py
1"""2Cartesian Genetic Programming3=============================4Genetic Programming is concerned with the automatic evolution (as in 5Darwinian evolution) of computational structures (such as mathematical 6equations, computer programs, digital circuits, etc.). CGP is a highly 7efficient and flexible form of Genetic Programming that encodes a graph 8representation of a computer program. It was developed by Julian Miller9in 1999. For more information see: http://cartesiangp.co.uk/10This script written by Petr Dvoracek in 2017. http://flea.name/11"""12import random 13from copy import deepcopy14# The main pillar of this algorithm lies in the directed oriented graph in15# which each node represents a specific operation. 16# The nodes are commonly arranged in square lattice.17DEFAULT_MATRIX_SIZE = (2, 5) # ROWS, COLS18# The interconnection between columns. 19DEFAULT_LEVEL_BACK = 5 # If the value is equal to number of columns then 20 # the interconnection is maximal.21# The operation set. 22# List of tuples(`str` name, `int` arity (inputs), `function` operation). 23DEFAULT_OPERATION_SET = set([("BUF", 1, lambda *arg: arg[0]),24 ("NOT", 1, lambda *arg: ~arg[0]), 25 ("NAND", 2, lambda *arg: ~(arg[0] & arg[1])), 26 ("NOR", 2, lambda *arg: ~(arg[0] | arg[1])), 27 ("AND", 2, lambda *arg: arg[0] & arg[1]), 28 ("OR", 2, lambda *arg: arg[0] | arg[1]),29 ("XOR", 2, lambda *arg: arg[0] ^ arg[1]), 30 ("XNOR", 2, lambda *arg: ~(arg[0] ^ arg[1])),31 ])32# Lets find full adder by default.33# For all input combinations define:34DEFAULT_INPUT_DATA = [0b00001111, # A35 0b00110011, # B36 0b01010101] # Carry in37# Define output combinations38DEFAULT_TRAINING_DATA = [0b01101001, # SUM39 0b00010111] # Carry out40# The CGP is a genetic algorithm. Therefore, we need to define evolutionary parameters.41# The number of candidate solutions.42DEFAULT_POP_SIZE = 5 43# Maximal generations (how long the algorithm will run)44DEFAULT_MAX_GENERATIONS = 10000 45# This function returns the list of objects with poistion `index` in the `nested_list` (list of arrays).46# Example: get_sublist(1, [("A", 3, 5), ("C", 1), ("D", "C")]) --> [3, 1, "C"]47get_sublist = lambda index, nested_list : map(lambda sublist: sublist[index], nested_list)48# How many genes will be mutated.49# 10% of the chromosome length can be mutated. Chromosome length = each_node * len(node) + len(last_node); The length of node is maximal arity + the operation.50DEFAULT_MUTATIONS = int((DEFAULT_MATRIX_SIZE[0] * DEFAULT_MATRIX_SIZE[1] * (max(get_sublist(1, DEFAULT_OPERATION_SET)) + 1) + len(DEFAULT_TRAINING_DATA)) * 0.10)51#DEFAULT_MUTATIONS = 452class CGP(object):53 # Printing if we find better fitness during the evolution.54 PRINT_INFO = False55 def __set_connections(self):56 """ Calculates the set of valid connections """57 # For each column create valid connection interval from previous `l-back` connections.58 self._valid_connection = [(self.rows * (column - self.level_back), self.rows * column) for column in xrange(self.columns)] 59 # Filter invalid indexes which are negative -> make them 060 self._valid_connection = map(lambda sublist: map(lambda x: x if (x >= 0) else 0, sublist), self._valid_connection)61 def __test_operation_set(self, operation_set):62 """ Check the validity of operation set.63 64 Parameters65 operation_set : list of operations66 Each opeartaion is a tuple(`str` name, `int` arity, `function` operation). 67 Raises68 CGPError69 If the operation set contains inconsistent operations.70 """71 72 # Test for empty set.73 if not operation_set:74 raise CGPError("EMPTY_SET")75 # Test if each operation has a triplet of (str, int, function) and tests the arity.76 for operation in operation_set:77 if len(operation) != 3 or type(operation[0]) is not str \78 or type(operation[1]) is not int \79 or not callable(operation[2]):80 raise CGPError("INCONSISTENT_SET", value=operation)81 # Negative arity of function is stupid. Unless, you are doing some deep shit math. 82 if operation[1] < 0: 83 raise CGPError("INCONSISTENT_ARITY", value=operation)84 def set_data(self, input_data, training_data):85 """ Set Input and output data86 87 Parameters88 input_data : list89 The list of input data. In the case of digital circuit design, 90 this contains all the input combinations. 91 training_data : list92 The target circuit.93 """94 self.input_data_range = range(-len(input_data), 0)95 self.input_data = input_data96 self.training_data = training_data97 def set_matrix(self, matrix, level_back=0):98 """ Initate the matrix. 99 Parameters100 matrix : tuple (int, int)101 This tuple represents the size of matrix (rows, columns)102 Other Parameters103 level_back : int104 The interconnection between columns.105 """106 self.matrix = matrix # Though, it is not used.107 self.rows = matrix[0]108 self.columns = matrix[1]109 self.level_back = level_back if level_back > 0 else matrix[1] # If `level_back` has invalid value (0 or less), then the interconnection is maximal. 110 self.__set_connections()111 112 def set_operation_set(self, operation_set):113 """ Set the operation set.114 Parameters115 operation_set : list of operations116 Each opeartaion is a tuple(`str` name, `int` arity, `function` operation). 117 Raises118 CGPError119 If the operation set contains inconsistent operations.120 """121 self.__test_operation_set(operation_set)122 self.operation_set = operation_set123 # Transpose the operation set data.124 self.operations = get_sublist(2, operation_set)125 self.operations_arity = get_sublist(1, operation_set)126 self.operations_names = get_sublist(0, operation_set)127 # Get maximal arity128 self.maximal_arity = max(self.operations_arity)129 130 def __init__(self, matrix, operation_set, input_data, training_data, level_back=0):131 """ Initate the matrix, node arrangement, operation set and i/o data. 132 Parameters133 matrix : tuple (int, int)134 This tuple represents the size of matrix (rows, columns)135 operation set : list of tuples (`str` name, `int` arity, `function` operation). 136 The operation set.137 input_data : list138 The list of input data. In the case of digital circuit design, 139 this contains all the input combinations. 140 training_data : list141 The target circuit.142 Other Parameters143 level_back : int144 The interconnection between columns.145 Raises146 CGPError147 If the operation set contains inconsistent operations.148 """149 super(CGP, self).__init__()150 151 self.set_matrix(matrix, level_back=level_back)152 self.set_operation_set(operation_set)153 self.set_data(input_data, training_data)154 def __create_chromosome(self):155 """ Initate the chromosome. 156 The chromsome encodes the interconnection of the CGP graph. See: http://www.oranchak.com/cgp/doc/fig2.jpg157 """158 # Init array for chromosome159 chromosome = []160 # This function selects random value from input data or previous node in the graph.161 connect = lambda connection: random.choice(self.input_data_range + range(connection[0], connection[1]))162 163 # Nodes may differ by their connection between columns. The interconnection is depended on the value of level back. 164 for column_index in xrange(self.columns):165 # Get valid connection range for nodes in the column166 valid_con = self._valid_connection[column_index]167 # Function for the creation of the node. Firstly creates the connections, then adds the node operation. 168 create_node = lambda: [connect(valid_con) for _ in xrange(self.maximal_arity)] + [random.choice(self.operations)]169 chromosome += [create_node() for _ in xrange(self.rows)]170 # Add last `node` which binds the primary outputs of the graph to nodes.171 valid_con = (0, len(chromosome))172 chromosome += [[connect(valid_con) for _ in self.training_data]]173 return chromosome174 def __init_pop(self, pop_size):175 """ Initiate the population of `pop_size` size """176 self.pop = [self.__create_chromosome() for _ in xrange(pop_size)]177 # The best individual is saved in `self.best_chrom` with the best fitness `self.fitness`.178 self.best_chrom = deepcopy(self.pop[0])179 self.best_fitness = self.max_fitness180 def __mutate_gene(self, chromosome):181 """ Mutate random value in a `chromsome`. 182 183 Parameters184 chromosome : chromsome strucutre185 The representation of candidate solution which is subject to a mutation. 186 """187 # This function selects random value from input data or previous node in the graph.188 connect = lambda connection: random.choice(self.input_data_range + range(connection[0], connection[1]))189 # Select some node190 random_node_idx = random.randint(0, len(chromosome) - 1)191 random_node = chromosome[random_node_idx]192 193 # And select an index from the node194 random_idx = random.randint(0, len(random_node) - 1) # Note: A <= rnd <= B195 previous_value = random_node[random_idx]196 if random_node is chromosome[-1]:197 # We mutate the last node198 while previous_value == random_node[random_idx]:199 random_node[random_idx] = connect((0, len(chromosome)))200 elif random_idx == self.maximal_arity:201 # We mutate the operation (indexing from 0)202 while previous_value == random_node[random_idx]:203 random_node[random_idx] = random.choice(self.operations)204 else:205 # We mutate the connection206 while previous_value == random_node[random_idx]:207 random_node[random_idx] = connect(self._valid_connection[ random_node_idx / self.rows ])208 def mutate(self):209 """ Creates a copy of the best solution and mutates it random-times.210 Return211 chromsome : chromosome structure212 The representation of the other candidate solution, which is similar to the best found solution.213 """214 # Copy the best chromsome215 chromosome = deepcopy(self.best_chrom)216 # And mutate it as much as possible217 for _ in xrange(random.randint(1, self.mutation_rate+1)): # Mutate it at least once. Else the chromsome wouldn't change.218 self.__mutate_gene(chromosome)219 return chromosome220 def init_eval(self):221 """ This function is started before the run of evolution. """222 # Buffer for the evaluation.223 buffer_data = deepcopy(self.input_data) + range(self.rows * self.columns + len(self.training_data))224 buffer_indexes = range(-len(self.input_data), len(buffer_data) - len(self.input_data))225 self.buffer = dict(zip(buffer_indexes, buffer_data))226 227 # Maximal fitness228 self.max_fitness = len(self.training_data) * (2 ** len(self.input_data))229 # Fitness mask.230 # V mask V The number of bites V # We are doing parallel evaluation. 231 self.bitmask = 2 ** (2 ** len(self.input_data)) - 1232 # Set generations.233 self.generation = 0234 def chrom_to_str(self, chromosome):235 """ Creates a string representation of chromosome, in which the nodes are aligned into a lattice. 236 Each node has syntax `index` : [`previous index`, ... , `previous index`, `Function name`] 237 If the node was primary output, then the node is ended with a string `<- `. 238 The primary outputs are also printed in the last line for the sake of their permutation. 239 Parameters240 chromsome : chromosome structure241 The representation of chromosome.242 Returns243 str244 The string representation of chromosome.245 """246 # Some math stuff247 max_input_strlen = len(str(self.columns * self.rows))248 max_length_of_operation_string = max(map(len, self.operations_names))249 chrom_str = ""250 for i in xrange(self.rows):251 line = ""252 for j in xrange(self.columns):253 # Grab the node index 254 node_idx = j * self.rows + i255 node = chromosome[node_idx]256 # Find the inputs, and align them.257 inputs = node[:-1] # The last node is operation; the rest are the inputs.258 aligned_inputs = map(lambda x: str(x).rjust(max_input_strlen), inputs)259 # Find out the operation name and align it.260 operation = node[-1]261 operation_index = self.operations.index(operation)262 operation_name = self.operations_names[operation_index]263 aligned_operation_name = operation_name.rjust(max_length_of_operation_string)264 # save it265 if node_idx in chromosome[-1]:266 flag = "<- "267 else:268 flag = " "269 line += str(node_idx).rjust(max_input_strlen) + ": [" + ", ".join(aligned_inputs) + ", " + aligned_operation_name + "]" + flag270 chrom_str += line + "\n"271 return chrom_str + str(chromosome[-1])272 273 def eval(self, chromosome):274 """ Evaluates the `chromosome` and returns the fitness value. Also checks if we found a better solution.275 Parameters276 chromsome : chromosome structure277 The representation of a candidate solution.278 Returns279 int : fitness value280 The quality of the candidate solution.281 TODO282 Optimise it. Skip neutral mutations. Skip unused nodes. (Maybe use C/C++ function?)283 Move the fitness check 284 """285 fitness = 0 286 # Evaluate each node.287 for idx, node in enumerate(chromosome[:-1]):288 # TODO SKIP unused nodes289 # Save the value of the operation `node[-1]`. The arguments are given in `node[:-1]` and they are the indexes into the `buffer`.290 self.buffer[idx] = node[-1](*[self.buffer[i] for i in node[:-1]])291 # Grab the value 292 for idx, target in zip(chromosome[-1], self.training_data):293 #print idx, target, self.bitmask294 fitness += bin((self.buffer[idx] ^ target) & self.bitmask).count("1")295 # Checks if we found a better solution.296 # Shouldnt be here.297 if fitness <= self.best_fitness:298 if CGP.PRINT_INFO and fitness < self.best_fitness:299 print "Generation: " + str(self.generation).rjust(10) + "\tFitness: " + str(fitness)300 self.best_fitness = fitness301 self.best_chrom = deepcopy(chromosome)302 return fitness303 def run(self, pop_size, mutations, generations):304 """ Runs the evolution.305 Parameters306 chromsome : chromosome structure307 The representation of chromosome.308 """309 # Init evaluation310 self.init_eval()311 # Init mutation312 self.mutation_rate = mutations313 # Creates first population `self.pop` and evaluates it `self.eval`.314 self.__init_pop(pop_size)315 # Run evolution316 for self.generation in xrange(generations):317 # Evaluate pop318 map(self.eval, self.pop)319 # Mutate pop; fix the peroformance 320 new_pop = [self.mutate() for _ in self.pop]321 self.pop = new_pop322 #break323 324 def __str__(self):325 return self.chrom_to_str(self.best_chrom)326 327class CGPError(Exception):328 def __init__(self, error_code, value=""):329 """ Inits the exception. """330 self.error_code = error_code331 self.value = value332 def __str__(self):333 """ Print error message, depending on the given error code. """334 error_msg = {335 "EMPTY_SET" : "The operation set is empty.",336 "INCONSISTENT_SET" : "The operation set contains inconsistent operation: " + repr(self.value) + "\n"\337 + "Please use triplet (`str` name, `int` arity, `function` operation)",338 "INCONSISTENT_ARITY" : "The arity of operation " + repr(self.value) +" can not be negative.", # Note: if you are doing some high math sh.t, then write your own CGP with blackjack and h..kers.339 }.get(self.error_code, "Unknown error - " + repr(self.error_code))340 return error_msg341if __name__ == '__main__':342 CGP.PRINT_INFO = True343 cgp = CGP(DEFAULT_MATRIX_SIZE, DEFAULT_OPERATION_SET, DEFAULT_INPUT_DATA, DEFAULT_TRAINING_DATA)344 cgp.run(DEFAULT_POP_SIZE, DEFAULT_MUTATIONS, DEFAULT_MAX_GENERATIONS)...
base.py
Source:base.py
...60 self.client = DatabaseClient(self)61 self.creation = DatabaseCreation(self)62 self.introspection = DatabaseIntrospection(self)63 self.validation = DatabaseValidation()64 def _valid_connection(self):65 if self.connection is not None:66 try:67 self.connection.ping()68 return True69 except DatabaseError:70 self.connection.close()71 self.connection = None72 return False73 def _cursor(self):74 if not self._valid_connection():75 kwargs = {76 #'conv': django_conversions,77 'charset': 'utf8',78 'use_unicode': True,79 }80 settings_dict = self.settings_dict81 if settings_dict['DATABASE_USER']:82 kwargs['user'] = settings_dict['DATABASE_USER']83 if settings_dict['DATABASE_NAME']:84 kwargs['db'] = settings_dict['DATABASE_NAME']85 if settings_dict['DATABASE_PASSWORD']:86 kwargs['passwd'] = settings_dict['DATABASE_PASSWORD']87 if settings_dict['DATABASE_HOST'].startswith('/'):88 kwargs['unix_socket'] = settings_dict['DATABASE_HOST']89 elif settings_dict['DATABASE_HOST']:90 kwargs['host'] = settings_dict['DATABASE_HOST']91 if settings_dict['DATABASE_PORT']:92 kwargs['port'] = int(settings_dict['DATABASE_PORT'])93 kwargs.update(settings_dict['DATABASE_OPTIONS'])94 self.connection = eventlet.db_pool.ConnectionPool.connect(MySQLdb, 15, **kwargs)95 connection_created.send(sender=self.__class__)96 cursor = mysqldb_base.CursorWrapper(self.connection.cursor())97 return cursor98 def _rollback(self):99 try:100 BaseDatabaseWrapper._rollback(self)101 except Database.NotSupportedError:102 pass103 def get_server_version(self):104 if not self.server_version:105 if not self._valid_connection():106 self.cursor()107 self.server_version = self.connection._obj.get_server_info()108 #m = server_version_re.match(self.connection.get_server_version())109 #if not m:110 # raise Exception('Unable to determine MySQL version from version string %r' % self.connection.get_server_version())111 #self.server_version = tuple([int(x) for x in m.groups()])...
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