def MNIST_binary_inducing_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain_full = d['train_Y']
Xtest = d['test_X']
Ytest_full = d['test_Y']
name = 'mnist_binary'
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
Ytrain = np.apply_along_axis(
lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1,
Ytrain_full).astype(int)[:, np.newaxis]
Ytest = np.apply_along_axis(
lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1,
Ytest_full).astype(int)[:, np.newaxis]
kernel = [
ExtRBF(Xtrain.shape[1],
variance=11,
lengthscale=np.array((9., )),
ARD=False) for j in range(1)
]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = LogisticLL()
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp', 'inducing'],
IdentityTransformation,
False,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 60,
'hyp': 15,
'inducing': 6
},
max_iter=9,
n_threads=n_threads,
ftol=10,
model_image_file=image,
partition_size=partition_size))
def MNIST_binary_inducing_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain_full = d['train_Y']
Xtest = d['test_X']
Ytest_full = d['test_Y']
name = 'mnist_binary'
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
Ytrain = np.apply_along_axis(lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1, Ytrain_full).astype(int)[:, np.newaxis]
Ytest = np.apply_along_axis(lambda x: x[1:10:2].sum() - x[0:10:2].sum(), 1, Ytest_full).astype(int)[:, np.newaxis]
kernel = [ExtRBF(Xtrain.shape[1], variance=11, lengthscale=np.array((9.,)), ARD=False) for j in range(1)]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = LogisticLL()
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp', 'inducing'], IdentityTransformation, False,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 60, 'hyp': 15, 'inducing': 6},
max_iter=9, n_threads=n_threads, ftol=10,
model_image_file=image, partition_size=partition_size))
def MNIST_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'mnist'
# uncomment these lines to delete unused features
# features_rm = np.array([])
# for n in range(Xtrain.shape[1]):
# if Xtrain[:, n].sum() ==0:
# features_rm = np.append(features_rm, n)
# Xtrain = np.delete(Xtrain, features_rm.astype(int), 1)
# Xtest = np.delete(Xtest, features_rm.astype(int), 1)
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
kernel = [
ExtRBF(Xtrain.shape[1],
variance=11,
lengthscale=np.array((9., )),
ARD=False) for j in range(10)
]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = SoftmaxLL(10)
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest,
Xtrain,
Ytest,
Ytrain,
cond_ll,
kernel,
method,
name,
d['id'],
num_inducing,
num_samples,
sparsify_factor, ['mog', 'hyp'],
IdentityTransformation,
False,
config['log_level'],
False,
latent_noise=0.001,
opt_per_iter={
'mog': 50,
'hyp': 10
},
max_iter=300,
n_threads=n_threads,
ftol=10,
model_image_file=image,
partition_size=partition_size))
def MNIST_data(config):
method = config['method']
sparsify_factor = config['sparse_factor']
np.random.seed(12000)
data = DataSource.mnist_data()
names = []
d = data[config['run_id'] - 1]
Xtrain = d['train_X']
Ytrain = d['train_Y']
Xtest = d['test_X']
Ytest = d['test_Y']
name = 'mnist'
# uncomment these lines to delete unused features
# features_rm = np.array([])
# for n in range(Xtrain.shape[1]):
# if Xtrain[:, n].sum() ==0:
# features_rm = np.append(features_rm, n)
# Xtrain = np.delete(Xtrain, features_rm.astype(int), 1)
# Xtest = np.delete(Xtest, features_rm.astype(int), 1)
# uncomment these lines to change the resolution
# res = 13
# current_res = int(np.sqrt(Xtrain.shape[1]))
# X_train_resized = np.empty((Xtrain.shape[0], res * res))
# X_test_resized = np.empty((Xtest.shape[0], res * res))
# for n in range(Xtrain.shape[0]):
# im = Image.fromarray(Xtrain[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_train_resized[n] = np.array(im).flatten()
#
# for n in range(Xtest.shape[0]):
# im = Image.fromarray(Xtest[n, :].reshape((current_res, current_res)))
# im = im.resize((res, res))
# X_test_resized[n] = np.array(im).flatten()
#
#
# Xtrain = X_train_resized
# Xtest = X_test_resized
kernel = [ExtRBF(Xtrain.shape[1], variance=11, lengthscale=np.array((9.,)), ARD=False) for j in range(10)]
# number of inducing points
num_inducing = int(Xtrain.shape[0] * sparsify_factor)
num_samples = 2000
cond_ll = SoftmaxLL(10)
if 'n_thread' in config.keys():
n_threads = config['n_thread']
else:
n_threads = 1
if 'partition_size' in config.keys():
partition_size = config['partition_size']
else:
partition_size = 3000
image = None
if 'image' in config.keys():
image = config['image']
names.append(
ModelLearn.run_model(Xtest, Xtrain, Ytest, Ytrain, cond_ll, kernel, method, name, d['id'], num_inducing,
num_samples, sparsify_factor, ['mog', 'hyp'], IdentityTransformation, False,
config['log_level'], False, latent_noise=0.001,
opt_per_iter={'mog': 50, 'hyp': 10},
max_iter=300, n_threads=n_threads, ftol=10,
model_image_file=image, partition_size=partition_size))
