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embodied_ising_repeat_onegen.py
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embodied_ising_repeat_onegen.py
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import plotting
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 floor
from math import radians
from random import random
from random import sample
from random import randint
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
#import random
#from tqdm import tqdm
# ------------------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
# --- CLASSES ------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
# ------------------------------------------------------------------------------+
settings = {}
class ising:
# Initialize the network
def __init__(self, settings, netsize, Nsensors=2, Nmotors=2, name=None): # 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.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
self.Beta = settings['init_beta']
#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.assign_critical_values(settings)
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_dorg = np.random.rand() * self.maxRange
self.s[2] = np.tanh(random_dorg) * 2 - 1
def randomize_position(self, settings):
'''
Only used in TimeEvolve2
'''
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:]
self.v += (np.sum(self.s[-self.Msize1:]) / 2) * settings['dv_max'] * settings['dt']
# TODO: Negative values for velocity????
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[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
# 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 SequentialGlauberStep(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):
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)
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):
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)))
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)
def assign_critical_values(self, settings):
# LOAD ISING CORRELATIONS
# filename = 'correlations-ising2D-size400.npy'
# Cdist = np.load(filename)
Cdist = settings['Cdist']
self.m1 = np.zeros(self.size)
self.C1 = np.zeros((self.size, self.size))
for ii in range(self.size):
for jj in range(max(ii + 1, self.Ssize), self.size):
ind = np.random.randint(len(Cdist))
self.C1[ii, jj] = Cdist[ind]
# re-sort the assigned correlations from the critical ising model so that their order matches the order of the
# actual correlations
def sort_critical_correlations(self):
c = self.C
x = np.arange(np.prod(c.shape)).reshape(c.shape)[self.maskJ] # index vector
c = c[self.maskJ]
c1 = self.C1[self.maskJ]
orderc = np.argsort(c)
orderc1 = np.argsort(c1)
C1_new = np.zeros((self.size, self.size))
# loop through index vector and re-sort assigned correlations to match order of actual correlations
# for iEdge, index in enumerate(x):
# i_index = int(np.floor(index / self.size))
# j_index = int(index % self.size)
#
# condition = np.subtract(orderc1, orderc[iEdge]) == 0
# # C1_index = int(np.extract(condition, orderc1))
# # C1_new[i_index, j_index] = c1[C1_index]
#
# C1_new[i_index, j_index] = c1[condition]
# # C1_new[i_index, j_index] = c1[orderc1[condition]]
for i, iEdge in enumerate(orderc):
index = x[iEdge]
i_index = int(np.floor(index / self.size))
j_index = int(index % self.size)
# condition = np.subtract(orderc1, orderc[i]) == 0
C1_new[i_index, j_index] = c1[orderc1[i]]
self.C1 = C1_new
# 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 settingsmore 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 adge 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
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)
# 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) # 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)
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:
# 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
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?
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 dist(x1, y1, x2, y2):
return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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])
else:
isings_info.append([I.xpos, I.ypos, I.r, I.fitness])
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 = 0):
if settings['energy_model']:
for I in isings:
I.energies = [] # Clear .energies, that .avg_energy is calculated from with each iteration
I.energy = settings['initial_energy'] # Setting initial energy
T = settings['TimeSteps']
for I in isings:
I.position = np.zeros((2, T))
# Main simulation loop:
if settings['plot'] == True:
#plt.clf()
# plt.ion()
fig, ax = plt.subplots()
#fig.set_size_inches(15, 10)
isings_all_timesteps = []
foods_all_timesteps = []
#artists_all_TS = np.zeros(T)
#artist_list = []
'''
!!! iterating through timesteps
'''
#for t in tqdm(range(T)):
for t in range(T):
#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)
#plotting.design_figure(settings, fig, ax)
#plotting.initial_plot(isings, foods, settings, ax)
#artist_list.append(plotting.create_artists_append(isings, foods, settings))
#artists_all_TS.append(ax.artists))
#artists_all_TS[t] = copy.deepcopy(ax.artists)
#ax.cla()
'''
can optimize interact with matrix calculations instead of loops
'''
interact(settings, isings, foods)
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)
else:
for I in isings:
I.SequentialGlauberStep(settings)
if settings['plot']:
#plotting.animate_plot(artist_list, settings, ax, fig)
# try:
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
'''
def parallelizedSequGlauberSteps(isings, settings, asynchronous = False):
if not asynchronous:
if settings['cores'] == 0:
pool = mp.Pool(mp.cpu_count() - 1)
else:
pool = mp.Pool(settings['cores'])
if not asynchronous:
for I in isings:
I.UpdateSensors(settings) # update sensors at beginning
# pass_vars = (settings, I.Ssize, I.size, I.s, I.h, I.J, I.Beta)
# pool.apply_async(parallelizedSequGlauberStep, args=(pass_vars), callback=collect_result)
vars_list = [(settings, I.Ssize, I.size, I.s, I.h, I.J, I.Beta) for I in isings]
s_list = pool.map(parallelizedSequGlauberStep, vars_list)
pool.close()
#pool.join()
# = results
for i, I in enumerate(isings):
I.s = s_list[i]
I.Move(settings) # move organism at end
else:
for I in isings:
I.UpdateSensors(settings) # update sensors at beginning
# pass_vars = (settings, I.Ssize, I.size, I.s, I.h, I.J, I.Beta)
# pool.apply_async(parallelizedSequGlauberStep, args=(pass_vars), callback=collect_result)
vars_list = [(settings, I.Ssize, I.size, I.s, I.h, I.J, I.Beta) for I in isings]
s_list = pool.map_async(parallelizedSequGlauberStep, vars_list)
pool.close()
pool.join()
def parallelizedSequGlauberStep(pass_vars):
settings, Ssize, size, s, h, J, Beta = pass_vars
thermalTime = int(settings['thermalTime'])
# update all other neurons a bunch of times
for j in range(thermalTime):
perms = np.random.permutation(range(Ssize, size))
for i in perms:
s_fac = GlauberStepParallel(i, s, h, J, Beta, size)
s[i] = s[i] * s_fac
return s
def GlauberStepParallel(i, s, h, J, Beta, size):
eDiff = 2 * s[i] * (h[i] + np.dot(J[i, :] + J[:, i], s))
if Beta * eDiff < np.log(1.0 / np.random.rand() - 1): # Glauber
return -1
else:
return 1
def collect_result(result):
global results
results.append(result)
def TimeEvolve2(isings, BetaFactor, settings, T):
'''
Used to calculae heat capacity in natural environment... GA is removed for thid pupose ????
'''
#--- POPULATE THE ENVIRONMENT WITH FOOD ---------------+
foods = []
for i in range(0, settings['food_num']):
foods.append(food(settings))
# INITIALIZATIONS
BetaOG = []
for ii, I in enumerate(isings):
# initialize variables
I.pm = np.zeros((2, 1)) # position
I.p2m = np.zeros((2, 1))
I.Em = 0 # np.zeros((1, T)) # mean Energy
I.E2m = 0 # np.zeros((1, T)) # mean Energy ** 2
I.m = 0
I.m2 = 0
# set temperature
BetaOG.append(I.Beta)
I.Beta = I.Beta * BetaFactor # scale by org's local temperature
# random config
I.randomize_position(settings)
I.randomize_state()
I.fitness = 0
# TIME EVOLUTION / MEASUREMENTS
print('Thermalizing...')
for t in range(int(T / 10)):
interact(settings, isings, foods) # interact with food, update raw sensor values.
I.SequentialGlauberStep(settings)
print('Beginning measurements...')
if settings['plot'] == True:
plt.clf()
plt.ion()
fig, ax = plt.subplots()
folder = 'test'
rep = 0
# fig.set_size_inches(15, 10)
for t in range(T):
# if (t % 50) == 0 or t == 0:
# print(t)
interact(settings, isings, foods) # interact with food, update raw sensor values.
if settings['plot'] == True and (t % settings['frameRate']) == 0:
plot_frame(settings, folder, fig, ax, isings, foods, t, rep)
plt.pause(1e-5)
plt.draw()
plt.cla()
if settings['diagnostics'] == True and (t % settings['frameRate']) == 0:
fitness = 0
for I in isings:
fitness += I.fitness
print('t : %4i | F: %4f' % (t, fitness))
for I in isings:
I.SequentialGlauberStep(settings) # download sensor data into neurons, think (therm.), move.
# don't include sensor neurons in measurements
# sus M
I.m += np.sum(I.s[I.Ssize:]) / float(T)
I.m2 += np.sum(I.s[I.Ssize:]) ** 2 / float(T)
# sus p
p = [I.xpos, I.ypos]
I.pm[:, 0] += np.divide(p, float(T))
I.p2m[:, 0] += np.power(p, 2) / float(T)
# heat spec.
E = -(np.dot(I.s[I.Ssize:], I.h[I.Ssize:]) +
np.dot(np.dot(I.s[I.Ssize:], I.J[I.Ssize:, I.Ssize:]), I.s[I.Ssize:]))
I.Em += E / float(T)
I.E2m += E ** 2 / float(T)
# reset betas back to original values in case of iterated use
for ii, I in enumerate(isings):
I.Beta = BetaOG[ii]
return isings
# Dynamical Critical Learning Algorithm for poising units in a critical state
def HomeostaticGradient(isings, foods, settings, folder, rep):
T = settings['TimeSteps']
for I in isings:
I.m = np.zeros(I.size)
I.c = np.zeros((I.size, I.size))
I.C = np.zeros((I.size, I.size))
# I.var = np.zeros(I.size)
I.position = np.zeros((2, T))
# Main simulation loop:
if settings['plot'] == True:
plt.clf()
plt.ion()
fig, ax = plt.subplots()
# fig.set_size_inches(15, 10)
# start_time = time.time()
for t in range(T):
# print('Time = ' + str(t))
# PLOT SIMULATION FRAME
if settings['plot'] == True and (t % settings['frameRate']) == 0:
plot_frame(settings, folder, fig, ax, isings, foods, t, rep)
plt.pause(1e-5)
plt.draw()
plt.cla()
# check_eat_food(settings, isings, foods)
# calc_closest_food(isings, foods)
interact(settings, isings, foods)
for I in isings:
I.SequentialGlauberStep(settings)
I.position[:, t] = [I.xpos, I.ypos]
I.m += I.s
for iS in range(I.size):
I.c[iS, iS + 1:] += I.s[iS] * I.s[iS + 1:]
# I.c[iS, iS] += I.s[iS] ** 2
# print('Time Elapsed: ' + str(int(time.time() - start_time)))
# print(isings[0].position[:,t])
# CALCULATE CORRELATIONS AND MEAN ACTIVATIONS TO GET: dh, dJ
for I in isings:
I.m /= T
I.c /= T
for iS in range(I.size):
I.C[iS, iS + 1:] = I.c[iS, iS + 1:] - I.m[iS] * I.m[iS + 1:]
# I.var[iS] = I.c[iS, iS] - I.m[iS] ** 2
# resort correlations C1 to match the order of that of the actual C
# TODO: Does this actually work the way it's supposed to?
# TODO: Multiply by the sign?
# TODO: frustrated critical ising models?
I.sort_critical_correlations()
I.dh = I.m1 - I.m
I.dJ = I.C1 - I.C