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compute-heat-capacity-generational-2-recorded.py
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#!/usr/bin/env python
from embodied_ising import ising
import numpy as np
from sys import argv
from os import path, makedirs
import pickle
import random
import glob
from numba import jit
from automatic_plot_helper import load_settings
from automatic_plot_helper import decompress_pickle
from ising_net_fitness_landscape import all_states
from ising_net_fitness_landscape import calculate_energies
# --- COMPUTE HEAT CAPACITY -------------------------------------------------------+
def main():
if len(argv) < 3:
print("Usage: " + argv[0] + " <sim> + <bind> + <gen>")
# loadfile = 'sim-20180131-145412'
# bind = 0 POsition in BetaVec
# iterNum = 0
loadfile = str(argv[1])
bind = int(argv[2]) #beta index
iterNum = int(argv[3]) #Generation numbers
settings = load_settings(loadfile)
R, thermal_time, beta_low, beta_high, beta_num, y_lim_high = settings['heat_capacity_props']
#R = 100 # Number of Repetitions, each initialising with new recorded sensor value
mode = 'MonteCarlo'
#Is there a bug here? Nbetas = 101 originally Nbetas = 102 solves index error?
Nbetas = beta_num
betas = 10 ** np.linspace(beta_low, beta_high, Nbetas)
loadstr = 'save/' + loadfile + '/isings/gen[' + str(iterNum) + ']-isings.pickle'
# print(iterNum)
try:
file = open(loadstr, 'rb')
isings = pickle.load(file)
file.close()
except FileNotFoundError:
# Looking for compressed ising file in case normal pickle file is not found
isings = decompress_pickle(loadstr)
size = isings[0].size # get size from first agent
numAgents = len(isings)
C = np.zeros((R, numAgents))
# tqdm(range(R))
for rep in range(R):
# filename = 'files/mode_' + mode + '-size_' + \
# str(size) + '-ind_' + str(rep) + '.npz'
# filename = 'parameters.npz'
# data = np.load(filename)
# I = ising(size)
# I.h = data['h'][()][(size, rep)]
# I.J = data['J'][()][(size, rep)]
agentNum = 0
for I in isings:
#TimeSteps in dream simulation T = 100000
betaVec = betas * I.Beta # scale by org's local temperature
# print(agentNum)
beta_new = betaVec[bind]
#I.randomize_state()
# Initialize sensors with randoms set of sensor values that have been recorded during simulation
initialize_sensors_from_record_randomize_neurons(I)
# Initialize network state with lowest energy network energy state
# TODO: This eats up most of the computation, make this JIT!!!
if settings['minimal_energy_initializatin_heat_cap']:
sensor_vals = I.s[0:(settings['nSensors'])]
permutated_states, permutated_states_with_sensors = all_states(I, settings, sensor_vals)
energies_perm = calculate_energies(I, settings, permutated_states_with_sensors)
i_min_energy = np.argmin(energies_perm)
min_energy_state = permutated_states_with_sensors[i_min_energy]
I.s = np.array(min_energy_state)
# Thermalisation to equilibrium before making energy measurements
#TODO LEave thermalization to equilibrium away before measurement?
# I.s = SequentialGlauberStepFast(int(10), I.s, I.h, I.J, I.Beta, I.Ssize, I.size)
# Measuring energy between Glaubersteps
I.s, Em, E2m = SequentialGlauberStepFast_calc_energy(thermal_time, I.s, I.h, I.J, beta_new, I.Ssize, I.size)
#Old, slow way of clculating it:
# for t in range(int(T / 10)):
# # thermal time steps to get ANN to equilibrium
# I.DreamSensorGlauberStep()
# for t in range(T):
# # thermal time steps, where Ennergy is recorded
# I.DreamSensorGlauberStep()
# ### Add these 3 lines o embodied ising for natural heat capacity
#
# E = -(np.dot(I.s, I.h) + np.dot(np.dot(I.s, I.J), I.s))
# Em += E / float(T) # <-- mean calculation??
# E2m += E ** 2 / float(T)
# # Why is this divided by T (total amount of time steps after thermalization)? --> mean calculation
# Claculate heat capacity
C[rep, agentNum] = beta_new ** 2 * (E2m - Em ** 2) / size
agentNum += 1
# print(np.mean(C, 0))
# TODO: CHANGE THIS SO THERE IS NO CONFLICT WITH OTHER DREAM HEAT CAP CALCULATION
folder = 'save/' + loadfile + '/C_recorded' + '/C_' + str(iterNum) + '/'
file = 'C-size_' + str(size) + '-Nbetas_' + \
str(Nbetas) + '-bind_' + str(bind) + '.npy'
filename = folder + file
if not path.exists(folder):
makedirs(folder)
np.save(filename, C)
def initialize_sensors_from_record_randomize_neurons(I):
'''
Initialize sensors with randoms set of sensor values that have been recorded during simulation
Randomize all other neurons
'''
s = np.random.randint(0, 2, I.size) * 2 - 1
s = np.array(s, dtype=float)
#all_recorded_inputs = from_list_of_arrs_to_arr(I.all_recorded_inputs)
rand_index = random.randint(0, len(I.all_recorded_inputs)-1)
chosen_sens_inputs = I.all_recorded_inputs[rand_index]
for i in range(len(chosen_sens_inputs)):
I.s[i] = chosen_sens_inputs[i]
I.s = s
if not len(chosen_sens_inputs) == I.Ssize:
raise Exception('''For some reason the number of sensors that
recorded values exist for is different from the sensor size saved in the settings''')
@jit(nopython=True)
def SequentialGlauberStepFast_calc_energy(thermalTime, s, h, J, Beta, Ssize, size):
'''
Energy calculation each thermal time step
'''
# TODO: After figuring the effect of thermalize sensors out delete this shit, slows everything down!
thermalize_sensors = False
if thermalize_sensors:
all_neurons_except_sens = np.arange(0, size)
else:
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]
Em = 0
E2m = 0
for i in range(thermalTime):
#perms = perms_list[i]
#Prepare a matrix of random variables for later use
# TODO: In previous dream heat cap calculation, the sensors were thermalized as well, while here they remain to have their values
if thermalize_sensors:
perms = np.random.permutation(np.arange(0, size))
#np.random.permutation(size)
else:
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]
# Record/Measure energy:
E = -(np.dot(s, h) + np.dot(np.dot(s, J), s))
Em += E / float(thermalTime) # <-- mean calculation??
E2m += E ** 2 / float(thermalTime)
return s, Em, E2m
@jit(nopython=True)
def SequentialGlauberStepFast(thermalTime, s, h, J, Beta, Ssize, size):
thermalize_sensors = False
if thermalize_sensors:
all_neurons_except_sens = np.arange(0, size)
else:
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
# TODO: In previous dream heat cap calculation, the sensors were thermalized as well, while here they remain to have their values
if thermalize_sensors:
perms = np.random.permutation(np.arange(0, size))
#perms = np.random.permutation(size)
else:
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
# def from_list_of_arrs_to_arr(arr_list):
# return np.concatenate(arr_list, axis=0)
if __name__ == '__main__':
main()