# below are the pre-calculated log likelihood (not negative!) values for l_INI, with alpha_INI = 10, 1, 0.01
# the more positive these values are, the more likely the sample follows max-min sampling
from cal_L_INI import cal_L_INI
l_INI_10 = -cal_L_INI(10.)
l_INI_1 = cal_L_INI(1.)
l_INI_001 = -cal_L_INI(.01)
sample_size = 100
num_ini_guess = 2
alpha = 10.0
soln = CovarianceEstimate(X, y, bounds=bounds, xbounds=xbounds, alpha=alpha, sample_size=sample_size,
num_ini_guess=num_ini_guess, initial_guess=initial_guess, l_INI=l_INI_10[:(n_trajectory-2)])
# x_temp =np.random.normal(initial_guess, scale=0.1, size=(1,31))
# # x_temp = np.ones((31,))*10.0
f0 = soln.model.obj(initial_guess, alpha=alpha, l_INI=l_INI_10[:(n_trajectory-2)])
print f0
# sig_test = np.zeros(31)
# sig_test[-1] = 2.6
# soln.model.f_path(sig_test)
[obj_set, sigma_set] = soln.solve(plot=False)
# # pick the best solution
obj = obj_set.min(axis=0)
sigma = sigma_set[obj_set.argmin(axis=0), :]
print obj, sigma
# # load bounds
{
'mix': pre.pca.mixing_.tolist(),
'unmix': pre.pca.components_.tolist(),
'mean': pre.pca.mean_.tolist()
},
outfile,
sort_keys=True,
indent=4,
ensure_ascii=False)
np.savetxt('mix_scaled_pWorse_init.txt',
X[:2]) # first two plays for later init.
bounds = np.array(31 * [[-1., 1.]])
soln = CovarianceEstimate(X, y, bounds=bounds, alpha=10.)
# sig_test = np.zeros(31)
# sig_test[-1] = 2.6
# soln.model.f_path(sig_test)
[obj_set, sigma_set] = soln.solve(plot=True)
# # pick the best solution
obj = obj_set.min(axis=0)
sigma = sigma_set[obj_set.argmin(axis=0), :]
print obj, sigma
# # load bounds
# from numpy import loadtxt
# bounds = loadtxt("ego_bounds.txt", comments="#", delimiter=",", unpack=False)
#
# # store sigma for simulation
0.15516951, 1.22674106, 0.65270824, 1.16261199, 1.38351062, 0.98991564,
1.62832714, 1.33700206, 1.32064881, 1.43865368, 0.84637371, 0.96145962,
1.86322818, 0.76696031, 1.39329787, 0.75810048, 1.59063005, 1.49758157,
0.86818537, 1.01713967, 1.21994997, 0.50326903, 0.26969321])
l_INI_001 = -np.array([ 0.0077654, 0.01148891, 0.0085491, 0.00846845, 0.00377641, 0.00445976,
0.00094088, 0.01169417, 0.00588484, 0.01099119, 0.01322844, 0.00925459,
0.0157014, 0.01277546, 0.01259828, 0.0137948, 0.00783676, 0.00910085,
0.01805538, 0.00703961, 0.01333193, 0.0069961, 0.01534839, 0.01438826,
0.00808909, 0.00955123, 0.01161055, 0.00448607, 0.00210488])
sample_size = 100
num_ini_guess = 2
alpha = 10.0
soln = CovarianceEstimate(X, y, bounds=bounds, xbounds=xbounds, alpha=alpha, sample_size=sample_size,
num_ini_guess=num_ini_guess, initial_guess=initial_guess, l_INI=l_INI_10[:(n_trajectory-2)])
# x_temp =np.random.normal(initial_guess, scale=0.1, size=(1,31))
# # x_temp = np.ones((31,))*10.0
f0 = soln.model.obj(initial_guess, alpha=alpha, l_INI=l_INI_10[:(n_trajectory-2)])
print f0
# sig_test = np.zeros(31)
# sig_test[-1] = 2.6
# soln.model.f_path(sig_test)
[obj_set, sigma_set] = soln.solve(plot=False)
# # pick the best solution
obj = obj_set.min(axis=0)
sigma = sigma_set[obj_set.argmin(axis=0), :]
print obj, sigma
# # load bounds
# # Parameters are there to speed up after saving a pkl.
pre = Preprocess(pca_model='../eco_full_pca.pkl', all_dat='../all_games.pkl')
# pre = Preprocess()
# pre.get_json('alluser_control.json') # uncomment this to create the pkl file needed!!
# pre.train_pca()
X, y = pre.ready_player_one(3)
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# scale = StandardScaler()
scale = MinMaxScaler((-1., 1.))
X = scale.fit_transform(X)
#
bounds = np.array(30 * [[-1., 1.]])
# # get sigma estimate that maximizes the sum of expected improvements
soln = CovarianceEstimate(X, y, bounds=bounds)
[obj_set, sigma_set] = soln.solve()
# # pick the best solution
obj = obj_set.min(axis=0)
sigma = sigma_set[obj_set.argmin(axis=0), :]
print obj, sigma
# # load bounds
# from numpy import loadtxt
# bounds = loadtxt("ego_bounds.txt", comments="#", delimiter=",", unpack=False)
#
# # store sigma for simulation
# # TODO: need to specify file name based on settings, e.g., optimization algorithm and input data source (best player?)
file_address = 'p3_slsqp_sigma_oldICA.json'
json.dump(temp.components_.tolist(), f, sort_keys=True, indent=4, ensure_ascii=False)
f.close()
with open('pWorse_range_transform.json', 'wb') as outfile:
json.dump({'range':scale.scale_.tolist(), 'min':scale.min_.tolist()},
outfile, sort_keys=True, indent=4, ensure_ascii=False)
with open('pWorse_ICA_transform.json', 'wb') as outfile:
json.dump({'mix':pre.pca.mixing_.tolist(), 'unmix':pre.pca.components_.tolist(), 'mean':pre.pca.mean_.tolist()},
outfile, sort_keys=True, indent=4, ensure_ascii=False)
np.savetxt('mix_scaled_pWorse_init.txt', X[:2]) # first two plays for later init.
bounds = np.array(31*[[-1., 1.]])
soln = CovarianceEstimate(X, y, bounds=bounds, alpha=10.)
# sig_test = np.zeros(31)
# sig_test[-1] = 2.6
# soln.model.f_path(sig_test)
[obj_set, sigma_set] = soln.solve(plot=True)
# # pick the best solution
obj = obj_set.min(axis=0)
sigma = sigma_set[obj_set.argmin(axis=0), :]
print obj, sigma
# # load bounds
# from numpy import loadtxt
# bounds = loadtxt("ego_bounds.txt", comments="#", delimiter=",", unpack=False)
#
# # store sigma for simulation
# # Parameters are there to speed up after saving a pkl.
pre = Preprocess(pca_model='../eco_full_pca.pkl', all_dat='../all_games.pkl')
# pre = Preprocess()
# pre.get_json('alluser_control.json') # uncomment this to create the pkl file needed!!
# pre.train_pca()
X, y = pre.ready_player_one(2)
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# scale = StandardScaler()
scale = MinMaxScaler((-1., 1.))
X = scale.fit_transform(X)
#
bounds = np.array(30*[[-1., 1.]])
# # get sigma estimate that maximizes the sum of expected improvements
soln = CovarianceEstimate(X, y, bounds=bounds)
[obj_set, sigma_set] = soln.solve()
# # pick the best solution
obj = obj_set.min(axis=0)
sigma = sigma_set[obj_set.argmin(axis=0), :]
print obj, sigma
# # load bounds
# from numpy import loadtxt
# bounds = loadtxt("ego_bounds.txt", comments="#", delimiter=",", unpack=False)
#
# # store sigma for simulation
# # TODO: need to specify file name based on settings, e.g., optimization algorithm and input data source (best player?)
file_address = 'p3_slsqp_sigma_oldICA.json'
# # delete the parameters if performing first-time or new player.
# # Parameters are there to speed up after saving a pkl.
pre = Preprocess(pca_model='eco_full_pca.pkl', all_dat='all_games.pkl')
# pre = Preprocess()
# pre.get_json('alluser_control.json') # uncomment this to create the pkl file needed!!
# pre.train_pca()
X, y = pre.ready_player_one(2)
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# scale = StandardScaler()
scale = MinMaxScaler((-1., 1.))
X = scale.fit_transform(X)
#
# # get sigma estimate that maximizes the sum of expected improvements
soln = CovarianceEstimate(X, y)
[obj_set, sigma_set] = soln.solve()
# # pick the best solution
obj = obj_set.min(axis=0)
sigma = sigma_set[obj_set.argmin(axis=0), :]
print obj, sigma
# # load bounds
# from numpy import loadtxt
# bounds = loadtxt("ego_bounds.txt", comments="#", delimiter=",", unpack=False)
#
# # store sigma for simulation
# # TODO: need to specify file name based on settings, e.g., optimization algorithm and input data source (best player?)
file_address = 'p3_slsqp_sigma_oldICA.json'
