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embodied_ising.py
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embodied_ising.py
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import compute_and_plot_heat_capacity_automatic
from embodied_ising_helper import animate
import numpy as np
import operator
from itertools import combinations, product
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import copy
from math import atan2
from math import cos
from math import degrees
from math import radians
from random import random
from random import sample
from math import sin
from math import sqrt
from random import uniform
from copy import deepcopy
import multiprocessing as mp
import sys
import os
import pickle
import time
from shutil import copyfile
from numba import jit
from os import listdir
from os.path import isfile, join
#import random
#from tqdm import tqdm
#from pympler import tracker
import visualize_in_model_natural_heat_capacity
import ray
import gzip
from embodied_ising_helper.speciation import speciation
from embodied_ising_helper.speciation import calculate_shared_fitness
# This is needed to initialize lowest energy network state, which is used for natural heat capacity calculations
# ------------------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
# --- CLASSES ------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
settings = {}
class ising:
# Initialize the network
def __init__(self, settings, netsize, Nsensors=2, Nmotors=2, name=None):
'''
For more attributes look at function reset_state, which is run at the start of every generation
More attributes are initialized there
'''
# Create ising model
self.size = netsize
self.Ssize = Nsensors # Number of sensors
self.Msize = Nmotors # Number of sensors
self.radius = settings['org_radius']
self.h = np.zeros(netsize) # TODO: is this bias, does this ever go over [0 0 0 0 0]???????
# self.J = np.zeros((self.size, self.size))
self.J = np.random.random((self.size, self.size))*2 - 1
self.J = (self.J + self.J.T) / 2 #Connectivity Matrix
np.fill_diagonal(self.J, 0)
self.max_weights = 2
self.maxRange = sqrt((settings['x_max'] - settings['x_min']) ** 2 +
(settings['y_max'] - settings['y_min']) ** 2)
self.v_max = settings['v_max']
self.food_num_env = settings['food_num']
self.randomize_state()
self.xpos = 0.0 #Position
self.ypos = 0.0
self.randomize_position(settings) #randomize position
# self.r = uniform(0, 360) # orientation [0, 360]
# self.v = uniform(0, settings['v_max']/3) # velocity [0, v_max]
# self.dv = uniform(-settings['dv_max'], settings['dv_max']) # dv
self.dx = 0
self.dy = 0
self.name = name
'''
initial beta
'''
if settings['diff_init_betas'] is None:
self.Beta = settings['init_beta']
else:
self.Beta = np.random.choice(settings['diff_init_betas'], 1)
#self.Beta = 1.0
# self.defaultT = max(100, netsize * 20)
self.Ssize1 = 1 # FOOD ROTATIONAL SENSOR: sigmoid(theta)
self.Ssize2 = 1 # FOOD DISTANCE SENSOR: sigmoid(distance)
self.Ssize3 = 1 # DIRECTIONAL NEIGHBOUR SENSOR: dot-product distance normalized, see self.org_sens
self.Msize1 = int(self.Msize/2) # dv motor neuron
# MASK USED FOR SETTINGS J/h TO 0
self.maskJ = np.ones((self.size, self.size), dtype=bool)
self.maskJ[0:self.Ssize, 0:self.Ssize] = False
self.maskJ[-self.Msize: -self.Msize] = False
self.maskJ[0:self.Ssize, -self.Msize:] = False
np.fill_diagonal(self.maskJ, 0)
self.maskJ = np.triu(self.maskJ)
self.J[~self.maskJ] = 0
# self.maskJtriu = np.triu(self.maskJ)
self.disconnect_hidden_neurons(settings)
self.maskh = np.ones(self.size, dtype=bool)
self.maskh[0:self.Ssize] = False
self.d_food = self.maxRange # distance to nearest food
self.r_food = 0 # orientation to nearest food
#self.org_sens = 0 # directional, 1/distance ** 2 weighted organism sensor
self.fitness = 0
self.energy = 0.0
self.food = 0
self.energies = [] #Allows for using median as well... Replace with adding parameter up for average in future to save memory? This array is deleted before saving to reduce file size
self.avg_energy = 0 #currently median implemented
self.all_velocity = 0
self.avg_velocity = 0
self.v = 0.0
self.generation = 0
self.time_steps = 0 # time_steps of current generation
###Attributes required for heat capacity calculation###
# Those two vectors include the internal energy of the organism with different altered betas
self.cumulative_int_energy_vec = np.array([])
self.cumulative_int_energy_vec_quad = np.array([])
# This vector includes the factors that the vbeta value has been altered with
self.beta_vec = np.array([])
# This vector includes all heat capacity values of the organism with different altered beta values
self.heat_capacity_vec = np.array([])
self.selected = False # Those, that were selected in previous generation and copied into current get this
self.species = 0 # INT species name
self.isolated_population = 0 # INT Isolated population name
self.shared_fitness = 0 # Fitness calculated by speciation algorithm
self.prev_mutation = 'init' # Previous Mutation, can either be 'init', 'copy', 'point' or 'mate'
#self.assign_critical_values(settings) (attribute ising.C1)
if not settings['BoidOn']:
self.Update(settings, 0)
def get_state(self, mode='all'):
if mode == 'all':
return self.s
elif mode == 'motors':
return self.s[-self.Msize:]
elif mode == 'sensors':
return self.s[0:self.Ssize]
elif mode == 'non-sensors':
return self.s[self.Ssize:]
elif mode == 'hidden':
return self.s[self.Ssize:-self.Msize]
def get_state_index(self, mode='all'):
return bool2int(0.5 * (self.get_state(mode) + 1))
# Randomize the state of the network
def randomize_state(self):
self.s = np.random.randint(0, 2, self.size) * 2 - 1
self.s = np.array(self.s, dtype=float)
# SEE SENSOR UPDATE
# random sensor states are generated by considering the sensor limitations
random_rfood = (np.random.rand() * 360) - 180
self.s[0] = random_rfood / 180
random_dfood = np.random.rand() * self.maxRange
self.s[1] = np.tanh(self.radius / (random_dfood ** 2 + 1e-6)) * 2 - 1
random_v = np.random.rand() * self.v_max
self.s[2] = np.tanh(random_v)
# random_energy = np.random.rand() * self.food_num_env
# TODO: Make this more flexible!!
random_energy = np.random.rand() * 12
self.s[3] = np.tanh(random_energy)
def randomize_position(self, settings):
self.xpos = uniform(settings['x_min'], settings['x_max']) # position (x)
self.ypos = uniform(settings['y_min'], settings['y_max']) # position (y)
if settings['BoidOn']:
self.v = (np.random.randn(2) * 2 - 1) * settings['v_max']
self.dv = (np.random.randn(2) * 2 - 1) * settings['dv_max']
self.dx = self.v[0] * settings['dt']
self.dy = self.v[1] * settings['dt']
# self.r = np.abs(np.arctan(self.ypos / self.xpos))
self.r = np.arctan2(self.v[1], self.v[0]) * 180 / np.pi
else:
self.r = np.random.rand() * 360
self.v = np.random.rand() * settings['v_max'] #TODO: This cannot work with huge v_max
self.dv = np.random.rand() * settings['dv_max']
self.dx = self.v * cos(radians(self.r)) * settings['dt']
self.dy = self.v * sin(radians(self.r)) * settings['dt']
# NOT USED
# # Set random bias to sets of units of the system
# def random_fields(self, max_weights=None):
# if max_weights is None:
# max_weights = self.max_weights
# self.h[self.Ssize:] = max_weights * (np.random.rand(self.size - self.Ssize) * 2 - 1)
# Set random connections to sets of units of the system
def random_wiring(self, max_weights=None): # Set random values for h and J
if max_weights is None:
max_weights = self.max_weights
for i in range(self.size):
for j in np.arange(i + 1, self.size):
if i < j and (i >= self.Ssize or j >= self.Ssize):
self.J[i, j] = (np.random.rand(1) * 2 - 1) * self.max_weights
def Move(self, settings):
# print(self.s[-2:])
# TODO: velocity coeffecient that can be mutated?
# UPDATE HEADING - Motor neuron s.[-self.Msize:self.Msize1]
self.r += (np.sum(self.s[-self.Msize:-self.Msize1]) / 2) * settings['dr_max'] * settings['dt']
self.r = self.r % 360
# UPDATE VELOCITY - Motor neuron s.[-self.Msize1:]
if settings['motor_neuron_acceleration']:
self.v += (np.sum(self.s[-self.Msize1:]) / 2) * settings['dv_max'] * settings['dt']
else:
v_new = (np.sum(self.s[-self.Msize1:]) / 2) * settings['v_max']
v_new_largest = v_new + settings['dv_max'] * settings['dt']
v_new_lowest = v_new - settings['dv_max'] * settings['dt']
if v_new > v_new_largest:
v_new = v_new_largest
if v_new < v_new_lowest:
v_new = v_new_lowest
self.v = v_new
if self.v < 0:
self.v = 0
if self.v > settings['v_max']:
self.v = settings['v_max']
if self.r > settings['r_max']:
self.r = settings['r_max']
if settings['energy_model']:
if self.energy >= (self.v * settings['cost_speed']) and self.v > settings['v_min']:
#if agend has enough energy and wants to go faster than min speed
self.energy -= self.v * settings['cost_speed']
elif self.v > settings['v_min']:
#if agned wants to go faster than min speed but does not have energy
self.v = settings['v_min']
self.all_velocity += self.v
# print('Velocity: ' + str(self.v) + str(self.s[-1]))
# UPDATE POSITION
self.dx = self.v * cos(radians(self.r)) * settings['dt']
self.dy = self.v * sin(radians(self.r)) * settings['dt']
self.xpos += self.dx
self.ypos += self.dy
# torus boundary conditions
# if abs(self.xpos) > settings['x_max']:
# self.xpos = -self.xpos
#
# if abs(self.ypos) > settings['y_max']:
# self.ypos = -self.ypos
self.xpos = (self.xpos + settings['x_max']) % settings['x_max']
self.ypos = (self.ypos + settings['y_max']) % settings['y_max']
def UpdateSensors(self, settings):
# self.s refers to the neuron state, which for sensor neurons is sensor input
# self.s[0] = sigmoid(self.r_food / 180)
# self.s[1] = sigmoid(self.d_food)
# normalize these values to be between -1 and 1
# TODO: make the numberators (gravitational constants part of the connectivity matrix so it can be mutated)
self.s[0] = self.r_food / 180 # self.r_food can only be -180:180
# self.s[1] = np.tanh(np.log10(self.radius / (self.d_food ** 2 + 1e-6))) # self.d_food goes from 0 to ~
# self.s[2] = np.tanh(np.log10(self.org_sens + 1e-10))
self.s[1] = np.tanh(self.radius / (self.d_food ** 2 + 1e-6))*2 - 1 # self.d_food goes from 0 to ~
#self.s[2] = np.tanh((self.org_sens))*2 - 1
self.s[2] = np.tanh(self.v)
self.s[3] = np.tanh(self.energy)
# TODO: define number of sensors here:
#settings['nSensors'] = 4
# print(self.s[0:3])
# Execute step of the Glauber algorithm to update the state of one unit
def GlauberStep(self, i=None):
'''
Utilizes: self.s, self.h, self.J
Modifies: self.s
'''
if i is None:
i = np.random.randint(self.size)
eDiff = 2 * self.s[i] * (self.h[i] + np.dot(self.J[i, :] + self.J[:, i], self.s))
#deltaE = E_f - E_i = -2 E_i = -2 * - SUM{J_ij*s_i*s_j}
#self.J[i, :] + self.J[:, i] are added because value in one of both halfs of J seperated by the diagonal is zero
if self.Beta * eDiff < np.log(1.0 / np.random.rand() - 1):
#transformed P = 1/(1+e^(deltaE* Beta)
self.s[i] = -self.s[i]
'''
# Execute step of the Glauber algorithm to update the state of one unit
# Faster version??
def GlauberStep(self, i=None):
#if i is None:
# i = np.random.randint(self.size) <-- commented out as not used
eDiff = np.multiply(np.multiply(2, self.s[i]), np.add(self.h[i], np.dot(np.add(self.J[i, :], self.J[:, i]), self.s)))
if np.multiply(self.Beta, eDiff) < np.log(1.0 / np.random.rand() - 1): # Glauber
self.s[i] = -self.s[i]
'''
# Execute time-step using an ANN algorithm to update the state of all units
def ANNStep(self):
# SIMPLE MLP
af = lambda x: np.tanh(x) # activation function
Jhm = self.J + np.transpose(self.J) # connectivity for hidden/motor layers
Jh = Jhm[:, self.Ssize:-self.Msize] # inputs to hidden neurons
Jm = Jhm[:, -self.Msize:] # inputs to motor neurons
# activate and update
new_h = af(np.dot(self.s, Jh))
self.s[self.Ssize:-self.Msize] = new_h
new_m = af(np.dot(self.s, Jm))
self.s[-self.Msize:] = new_m
# TODO: non-symmetric Jhm, need to change through to GA
# Compute energy difference between two states with a flip of spin i
def deltaE(self, i):
return 2 * (self.s[i] * self.h[i] + np.sum(
self.s[i] * (self.J[i, :] * self.s) + self.s[i] * (self.J[:, i] * self.s)))
# Update states of the agent from its sensors
def Update(self, settings, i=None):
if i is None:
i = np.random.randint(self.size)
if i == 0:
self.Move(settings)
self.UpdateSensors(settings)
elif i >= self.Ssize:
self.GlauberStep(i)
def SequentialUpdate(self, settings):
for i in np.random.permutation(self.size):
self.Update(settings, i)
# Update all states of the system without restricted influences
def SequentialGlauberStepFastHelper(self, settings):
thermalTime = int(settings['thermalTime'])
self.UpdateSensors(settings)
self.s = SequentialGlauberStepFast(thermalTime, self.s, self.h, self.J, self.Beta, self.Ssize, self.size)
self.Move(settings)
def SequentialGlauberStep(self, settings, thermal_time):
thermalTime = int(thermal_time)
self.UpdateSensors(settings) # update sensors at beginning
# update all other neurons a bunch of times
for j in range(thermalTime):
perms = np.random.permutation(range(self.Ssize, self.size))
#going through all neuron exceot sensors in random permutations
for i in perms:
#self.GlauberStep(i)
rand = np.random.rand()
GlauberStepFast(i, rand, self.s, self.h, self.J, self.Beta)
self.Move(settings) # move organism at end
# Update all states of the system without restricted influences
def ANNUpdate(self, settings):
thermalTime = int(settings['thermalTime'])
self.UpdateSensors(settings) # update sensors at beginning
# update all other neurons a bunch of times
for j in range(thermalTime):
self.ANNStep()
self.Move(settings) # move organism at end
# update everything except sensors
def NoSensorGlauberStep(self):
perms = np.random.permutation(range(self.Ssize, self.size))
for i in perms:
self.GlauberStep(i)
# update sensors using glauber steps (dream)
def DreamSensorGlauberStep(self):
# As permutation over complete network together with sensor neurons are taken, sensor neurons are thermalized as well
perms = np.random.permutation(self.size)
for i in perms:
self.GlauberStep(i)
# ensure that not all of the hidden neurons are connected to each other
def disconnect_hidden_neurons(self, settings):
numHNeurons = self.size - self.Ssize - self.Msize
perms = list(combinations(range(self.Ssize, self.Ssize + numHNeurons), 2))
numDisconnectedEdges = len(list(combinations(range(settings['numDisconnectedNeurons']), 2)))
# settings['numDisconnectedNeurons'] how many hidden neurons are disconnenced fromeach other
for i in range(0, numDisconnectedEdges):
nrand = np.random.randint(len(perms))
iIndex = perms[nrand][0]
jIndex = perms[nrand][1]
self.J[iIndex,jIndex] = 0
# self.J[jIndex, iIndex] = 0
self.maskJ[iIndex, jIndex] = False
# self.maskJ[jIndex, iIndex] = False
# self.maskJtriu = np.triu(self.maskJ)
# mutate the connectivity matrix of an organism by stochastically adding/removing an edge
def mutate(self, settings):
'''
3 Mutations happening at once:
CONNECTIVITY Mutations:
One of these things happen
- A new edge is removed (according to sparsity settings more or less likely)
- or added (if no adding is possible some random edge gets new edge weight)
EDGE MUTATIONS
currently in an edge mutation means, that the whole edge weight is replaced by a randomly generated weight
BETA Mutations
Beta is mutated
'''
# ADDS/REMOVES RANDOM EDGE DEPENDING ON SPARSITY SETTING, RANDOMLY MUTATES ANOTHER RANDOM EDGE
# expected number of disconnected edges
numDisconnectedEdges = len(list(combinations(range(settings['numDisconnectedNeurons']), 2)))
totalPossibleEdges = len(list(combinations(range(self.size - self.Ssize - self.Msize), 2)))
# number of (dis)connected edges
connected = copy.deepcopy(self.maskJ)
disconnected = ~connected #disconnected not connected
np.fill_diagonal(disconnected, 0)
disconnected = np.triu(disconnected)
# things that need to be connected and not flagged to change
connected[0:self.Ssize, :] = 0
connected[:, -self.Msize:] = 0
# things that need to be disconnected and not flagged to change
disconnected[0:self.Ssize, -self.Msize:] = 0
disconnected[0:self.Ssize, 0:self.Ssize] = 0
numEdges = np.sum(connected) #number of edges, that can actuall be disconnected (in beginning of simulatpn curr settings 3)
# positive value means too many edges, negative value means too little
edgeDiff = numEdges - (totalPossibleEdges - numDisconnectedEdges)
# edgeDiff = numEdges - numDisconnectedEdges
# TODO: investigate the empty connectivity matrix here
prob = sigmoid(edgeDiff) #for numDisconnectedNeurons=0 this means 0.5 --> equal probability of adding edge and removing edge # probability near 1 means random edge will be removed, near 0 means random edge added
rand = np.random.rand()
if prob >= rand:
# remove random edge
i, j = np.nonzero(connected) #Indecies of neurons connected by edges that can be disconnected
if len(i) > 0:
randindex = np.random.randint(0, len(i))
ii = i[randindex]
jj = j[randindex]
self.maskJ[ii, jj] = False
self.J[ii, jj] = 0
# TODO: is this a good way of making the code multi-purpose?
# try:
# self.C1[ii, jj] = 0
# except NameError:
# pass'
else:
print('Connectivity Matrix Empty! Mutation Blocked.')
else:
#looking for disconnected neurons that can be connected
# add random edge
i, j = np.nonzero(disconnected)
if len(i) > 0:
randindex = np.random.randint(0, len(i))
ii = i[randindex]
jj = j[randindex]
self.maskJ[ii, jj] = True
self.J[ii, jj] = np.random.uniform(-1, 1) * self.max_weights
# I.J[ii, jj] = np.random.uniform(np.min(I.J[I.Ssize:-I.Msize, I.Ssize:-I.Msize]) / 2,
# np.max(I.J[I.Ssize:-I.Msize, I.Ssize:-I.Msize]) * 2)
# try:
# self.C1[ii, jj] = settings['Cdist'][np.random.randint(0, len(settings['Cdist']))]
# except NameError:
# pass
else: # if connectivity matrix is full, just change an already existing edge
#This only happens, when alogorithm tries to add edge, but everything is connected
i, j = np.nonzero(connected)
randindex = np.random.randint(0, len(i))
ii = i[randindex]
jj = j[randindex]
self.J[ii, jj] = np.random.uniform(-1, 1) * self.max_weights
# MUTATE RANDOM EDGE
i, j = np.nonzero(self.maskJ)
randindex = np.random.randint(0, len(i))
ii = i[randindex]
jj = j[randindex]
self.J[ii, jj] = np.random.uniform(-1, 1) * self.max_weights
#Mutation of weights--> mutated weight is generated randomly from scratch
# MUTATE LOCAL TEMPERATURE
if settings['mutateB']:
deltaB = np.abs(np.random.normal(1, settings['sigB']))
self.Beta = self.Beta * deltaB #TODO mutate beta not by multiplying? How was Beta modified originally?
#TODO: ADDED POSIIBILITY OF RANDOM BETA TO GLOBALIZE SEARCH SPACE FOR BETA
if settings['beta_jump_mutations']:
if np.random.uniform(0, 1) < 0.1:
self.Beta = 10 ** np.random.uniform(-1, 1)
#biases GA pushing towards lower betas (artifical pressure to small betas)
# End of mutate (1)
def reset_state(self, settings):
# randomize internal state (not using self.random_state since it also randomizes sensors)
# TODO !!! THIS LINE SEEMS TO BE RESPONSIBLE FOR CHANGING HEAT CAPACITY PLOTS !!! This creats floats, when states are supposed to be ints!
# self.s = np.random.random(size=self.size) * 2 - 1
self.randomize_state()
# includes: #self.s = np.random.randint(0, 2, self.size) * 2 - 1
# randomize position (not using self.randomize_position function since it also randomizes velocity)
self.xpos = uniform(settings['x_min'], settings['x_max']) # position (x)
self.ypos = uniform(settings['y_min'], settings['y_max']) # position (y)
self.dv = 0
self.v = 0
self.ddr = 0
self.dr = 0
self.food = 0
self.fitness = 0
# cumulative internal energies, every entry in array represents cumulated int energies for one beta value
self.cumulative_int_energy_vec = np.array([])
self.cumulative_int_energy_vec_quad = np.array([])
self.beta_vec = np.array([])
self.all_recorded_inputs = [] # List of arrays For every time step the input value of every sensor is saved
if settings['energy_model']:
self.energies = [] # Clear .energies, that .avg_energy is calculated from with each iteration
self.energy = settings['initial_energy'] # Setting initial energy
self.avg_energy = 0
self.all_velocity = 0
self.avg_velocity = 0
#
# @jit(nopython=True)
# def SequentialGlauberStepFast(thermalTime, perms, random_vars, s, h, J, Beta):
# for j in range(thermalTime):
# for ind, i in enumerate(perms):
# rand = random_vars[ind]
# GlauberStepFast(i, rand, s, h, J, Beta)
#
# @jit(nopython=True)
# def GlauberStepFast(i, rand, s, h, J, Beta ):
# '''
# Utilizes: self.s, self.h, self.J
# Modifies: self.s
# '''
#
# eDiff = 2 * s[i] * (h[i] + np.dot(J[i, :] + J[:, i], s))
# #deltaE = E_f - E_i = -2 E_i = -2 * - SUM{J_ij*s_i*s_j}
# #self.J[i, :] + self.J[:, i] are added because value in one of both halfs of J seperated by the diagonal is zero
#
# if Beta * eDiff < np.log(1.0 / rand - 1):
# #transformed P = 1/(1+e^(deltaE* Beta)
# s[i] = -s[i] # TODO return s!!!!!!!
# @jit(nopython=True)
# def SequentialGlauberStepFast(thermalTime, perms_list, random_vars_list, s, h, J, Beta):
# for j in range(thermalTime):
# perms = perms_list[j]
# random_vars = random_vars_list[j]
# for ind, i in enumerate(perms):
# rand = random_vars[ind]
# eDiff = 2 * s[i] * (h[i] + np.dot(J[i, :] + J[:, i], s))
# #deltaE = E_f - E_i = -2 E_i = -2 * - SUM{J_ij*s_i*s_j}
# #self.J[i, :] + self.J[:, i] are added because value in one of both halfs of J seperated by the diagonal is zero
#
# if Beta * eDiff < np.log(1.0 / rand - 1):
# #transformed P = 1/(1+e^(deltaE* Beta)
# s[i] = -s[i]
# return s
@jit(nopython=True)
def SequentialGlauberStepFast(thermalTime, s, h, J, Beta, Ssize, size):
all_neurons_except_sens = np.arange(Ssize, size)
#perms_list = np.array([np.random.permutation(np.arange(Ssize, size)) for j in range(thermalTime)])
random_vars = np.random.rand(thermalTime, len(all_neurons_except_sens)) #[np.random.rand() for i in perms]
for i in range(thermalTime):
#perms = perms_list[i]
#Prepare a matrix of random variables for later use
perms = np.random.permutation(np.arange(Ssize, size))
for j, perm in enumerate(perms):
rand = random_vars[i, j]
eDiff = 2 * s[perm] * (h[perm] + np.dot(J[perm, :] + J[:, perm], s))
#deltaE = E_f - E_i = -2 E_i = -2 * - SUM{J_ij*s_i*s_j}
#self.J[i, :] + self.J[:, i] are added because value in one of both halfs of J seperated by the diagonal is zero
if Beta * eDiff < np.log(1.0 / rand - 1):
#transformed P = 1/(1+e^(deltaE* Beta)
s[perm] = -s[perm]
return s
class food():
def __init__(self, settings):
self.xpos = uniform(settings['x_min'], settings['x_max'])
self.ypos = uniform(settings['y_min'], settings['y_max'])
self.energy = settings['food_energy']
def respawn(self, settings):
self.xpos = uniform(settings['x_min'], settings['x_max'])
self.ypos = uniform(settings['y_min'], settings['y_max'])
self.energy = settings['food_energy']
# ------------------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
# --- FUNCTIONS ----------------------------------------------------------------+
# ------------------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
def save_whole_project(folder):
'''Copies complete code into simulation folder'''
cwd = os.getcwd()
onlyfiles = [f for f in listdir(cwd) if isfile(join(cwd, f))]
save_folder = folder + 'code/'
for file in onlyfiles:
save_code(save_folder, file)
def save_code(folder, filename):
src = filename
dst = folder + src
copyfile(src, dst)
def dist(x1, y1, x2, y2):
return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
#@jit(nopython=True)
def pdistance_pairwise(x0, x1, dimensions, food=False):
'''
Parameters
----------
x0, x1:
(vectorized) list of coordinates. Can be N-dimensional. e.g. x0 = [[0.5, 2.], [1.1, 3.8]].
dimensions:
size of the bounding box, array of length N. e.g. [8., 8.], [xmax - xmin, ymax - ymin].
food:
boolean signifying if the distance calculations are between organisms or between organisms and food. In the
latter case we don't need to compare it both ways around, in the former, theta_mat is a non-symmetric matrix.
Returns
-------
dist_mat:
upper triangle matrix of pairwise distances accounting for periodic boundaries
theta_mat:
full matrix of angles between each position accounting for periodic boundaries
'''
# get all unique pairs combinations
N1 = len(x0)
N2 = len(x1)
if food:
combo_index = list(product(np.arange(N1), np.arange(N2)))
else:
if not len(x0) == len(x1):
raise Exception('x0.shape[0] not equal to x1.shape[0] when comparing organisms.')
combo_index = list(combinations(np.arange(N1), 2))
Ii = np.array([x0[i[0]] for i in combo_index])
Ij = np.array([x1[i[1]] for i in combo_index])
# calculate distances accounting for periodic boundaries
# delta = np.abs(Ipostiled_seq - Ipostiled)
delta = Ij - Ii
delta = np.where(np.abs(delta) > 0.5 * dimensions, delta - np.sign(delta)*dimensions, delta)
dist_vec = np.sqrt((delta ** 2).sum(axis=-1))
theta_vec_ij = np.degrees(np.arctan2(delta[:, 1], delta[:, 0])) # from org i to org j
if not food:
theta_vec_ji = np.degrees(np.arctan2(-delta[:, 1], -delta[:, 0])) # from org j to org i
if food:
dist_mat = dist_vec.reshape(N1, N2)
else:
dist_mat = np.zeros((N1, N2))
theta_mat = np.zeros((N1, N2))
for ii, ind in enumerate(combo_index):
i = ind[0]
j = ind[1]
# can leave this as upper triangle since it's symmetric
if not food:
dist_mat[i, j] = dist_vec[ii]
# need to get a full matrix since eventually these angles are not symmetric
theta_mat[i, j] = theta_vec_ij[ii]
# if comparing org-to-org angles, need the other direction as well
if not food:
theta_mat[j, i] = theta_vec_ji[ii]
return dist_mat, theta_mat
def calc_heading(I, food):
d_x = food.xpos - I.xpos
d_y = food.ypos - I.ypos
theta_d = degrees(atan2(d_y, d_x)) - I.r
theta_d %= 360
# keep the angles between -180:180
if theta_d > 180:
theta_d -= 360
return theta_d
# Transform bool array into positive integer
def bool2int(x):
y = 0
for i, j in enumerate(np.array(x)[::-1]):
y += j * 2 ** i
return int(y)
# Transform positive integer into bit array
def bitfield(n, size):
x = [int(x) for x in bin(int(n))[2:]]
x = [0] * (size - len(x)) + x
return np.array(x)
def extract_plot_information(isings, foods, settings):
isings_info = []
foods_info = []
for I in isings:
if settings['energy_model']:
isings_info.append([I.xpos, I.ypos, I.r, I.energy, I.isolated_population, I.species])
else:
isings_info.append([I.xpos, I.ypos, I.r, I.fitness, I.isolated_population, I.species])
for f in foods:
foods_info.append([f.xpos, f.ypos])
return isings_info, foods_info
def TimeEvolve(isings, foods, settings, folder, rep, total_timesteps, nat_heat_gens, beta_facs, calc_heat_cap_boo,
record, save_energies_velocities):
[ising.reset_state(settings) for ising in isings]
if settings['random_time_steps_power_law']:
low_limit, high_limit, a = settings['random_time_steps_power_law_limits']
T = int((1-np.random.power(a))*high_limit + low_limit)
elif settings['random_time_steps']:
random_ts_limits = settings['random_time_step_limits']
T = np.random.randint(random_ts_limits[0], random_ts_limits[1])
else:
T = settings['TimeSteps']
for I in isings:
I.time_steps = T
for I in isings:
I.position = np.zeros((2, T))
# Main simulation loop:
if settings['plot'] == True:
fig, ax = plt.subplots()
#fig.set_size_inches(15, 10)
isings_all_timesteps = []
foods_all_timesteps = []
# This switches on natural heat capacity calculations
'''
!!! iterating through timesteps
'''
#for t in tqdm(range(T)):
for t in range(T):
#TODO: Is it good to randomize neuron states each time step? (Not done before)
#[I.randomize_state() for I in isings]
#print(len(foods))
# print('\r', 'Iteration {0} of {1}'.format(t, T), end='') #, end='\r'
# print('\r', 'Tstep {0}/{1}'.format(t, T), end='') # , end='\r'
if not (settings['chg_food_gen'] is None):
if t == settings['chg_food_gen'][0]:
settings['num_food'] = settings['chg_food_gen'][1]
if settings['seasons'] == True:
foods = seasons(settings, foods, t, T, total_timesteps)
# PLOT SIMULATION FRAME
if settings['plot'] == True and (t % settings['frameRate']) == 0:
#plot_frame(settings, folder, fig, ax, isings, foods, t, rep)
isings_info, foods_info = extract_plot_information(isings, foods, settings)
isings_all_timesteps.append(isings_info)
foods_all_timesteps.append(foods_info)
interact(settings, isings, foods)
if save_energies_velocities:
for I in isings:
I.velocities.append(I.v)
if record:
num_sensors = settings['nSensors']
for I in isings:
all_recorded_inputs = I.all_recorded_inputs
# TODO: does this work as intended?:
recorded_input = I.s[:num_sensors]
all_recorded_inputs.append(recorded_input)
I.all_recorded_inputs = all_recorded_inputs
# Before normal thermalization, prepare_natural_heat_capacity does dream-state thermalization with different
# beta values and calculates heat-capacity
if calc_heat_cap_boo:
prepare_natural_heat_capacity(settings, isings, beta_facs)
if settings['BoidOn']:
boid_update(isings, settings)
for I in isings:
I.position[:, t] = [I.xpos, I.ypos]
else:
#parallelization here
if settings['ANN']:
I.ANNUpdate(settings)
else:
if settings['parallel_computing']:
# parallelizedSequGlauberSteps(isings, settings)
ray.init(num_cpus=settings['cores'])
ray_funcs = [ray_parallel_Glauber_steps.remote(I, settings) for I in isings]
ray.get(ray_funcs)
else:
[I.SequentialGlauberStepFastHelper(settings) for I in isings]
if calc_heat_cap_boo:
calculate_natural_heat_capacity(isings, T, beta_facs)
#try:
# except Exception:
# print('Could not create plots for natural heat capacity for generation {}'.format(rep))
if settings['plot']:
#plotting.animate_plot(artist_list, settings, ax, fig)
# try:
# if settings['fading_traces_animation']:
animate.animate_plot_Func(isings_all_timesteps, foods_all_timesteps, settings, ax, fig, rep, t, folder)
# else:
# plotting.animate_plot_Func(isings_all_timesteps, foods_all_timesteps, settings, ax, fig, rep, t, folder)
# except Exception:
# print('There occurred an error during animation...the simulation keeps going')
'''
for I in isings:
if settings['ANN']:
I.ANNUpdate(settings)
else:
I.SequentialGlauberStep(settings)
I.position[:, t] = [I.xpos, I.ypos]
'''
'''
#Helper functions parallelization
def parallelSequGlauberStep(I, settings):
# I = copy.deepcopy(I)
I.SequentialGlauberStep()
return I
'''
########## Functions for natural heat capacity calculations ##############
def calculate_natural_heat_capacity(isings, time_steps, beta_facs):
'''
Calculate the heat capacity at the end of every generation, utuliozing the ising attribute vectors created by
repare_natural_heat_capacity()
Input:
time_steps: amount of total time steps. Used to create mean from cumulative vectors
'''
for I in isings: