def accuracy(p, l):
import minpy.numpy as np
if len(l.shape) == 1:
return 1 - np.count_nonzero(p - l).val / float(p.shape[0])
else:
inputs, labels = p, l
return np.mean(np.sum((inputs - labels)**2, axis=1))
def discount_rewards(self, rs):
drs = np.zeros_like(rs).asnumpy()
s = 0
for t in reversed(range(0, len(rs))):
# Reset the running sum at a game boundary.
if rs[t] != 0:
s = 0
s = s * self.gamma + rs[t]
drs[t] = s
drs -= np.mean(drs)
drs /= np.std(drs)
return drs
def attack_mnist(model, alpha=0.2, beta=0.001, isTarget=False, num_attacks=10):
imgs = np.array(
nnp.load('data/mnist_data.npy')[60000:].transpose(
(0, 2, 3, 1)).astype(np.float32) / 255.)
labs = nnp.load('data/mnist_labels.npy')[60000:]
nb_labs = nnp.max(labs)
print(
"\n\n Running {} attack on {} random MNIST test images for alpha= {} beta= {}\n\n"
.format("targetted" if isTarget else "untargetted", num_attacks, alpha,
beta))
total_distortion = []
samples = []
for i in range(nb_labs + 1):
samples.append(
np.random.permutation(np.arange(len(labs))[labs == i])[0])
# samples = [6312, 6891, 4243, 8377, 7962, 6635, 4970, 7809, 5867, 9559, 3579, 8269, 2282, 4618, 2290, 1554, 4105, 9862, 2408, 5082, 1619, 1209, 5410, 7736, 9172, 1650, 5181, 3351, 9053, 7816, 7254, 8542, 4268, 1021, 8990, 231, 1529, 6535, 19, 8087, 5459, 3997, 5329, 1032, 3131, 9299, 3910, 2335, 8897, 7340, 1495, 5244,8323, 8017, 1787, 4939, 9032, 4770, 2045, 8970, 5452, 8853, 3330, 9883, 8966, 9628, 4713, 7291, 9770, 6307, 5195, 9432, 3967, 4757, 3013, 3103, 3060, 541, 4261, 7808, 1132, 1472, 2134, 634, 1315, 8858, 6411, 8595, 4516, 8550, 3859, 3526]
#true_labels = [3, 1, 6, 6, 9, 2, 7, 5, 5, 3, 3, 4, 5, 6, 7, 9, 1, 6, 3, 4, 0, 6, 5, 9, 7, 0, 3, 1, 6, 6, 9, 6, 4, 7, 6, 3, 4, 3, 4, 3, 0, 7, 3, 5, 3, 9, 3, 1, 9, 1, 3, 0, 2, 9, 9, 2, 2, 3, 3, 3, 0, 5, 2, 5, 2, 7, 2, 2, 5, 7, 4, 9, 9, 0, 0, 7, 9, 4, 5, 5, 2, 3, 5, 9, 3, 0, 9, 0, 1, 2, 9, 9]
for idx in samples:
#idx = random.randint(100, len(test_dataset)-1)
image, label = imgs[idx], labs[idx]
print("\n\n\n\n======== Image %d =========" % idx)
print("Original label: ", label)
lab = predict(model, image)
print("Predicted label: ", lab)
if lab != label:
print(
'CHANGE IMAGES#{}: prediction of original image is not the same with true label'
.format(i))
continue
#target = None if not isTarget else random.choice(list(range(label)) + list(range(label+1, 10)))
advs = [image]
for i in range(nb_labs):
target = (label + i) % (nb_labs + 1)
adv = attack_targeted(model,
imgs[labs == target],
image,
label,
target,
alpha=alpha,
beta=beta,
iterations=1000)
print(i, "Predicted label for adversarial example: ",
predict(model, adversarial))
advs.append(np.clip(adv, 0, 1))
total_distortion.append(
np.linalg.norm(adv.reshape(-1) - image.reshape(-1)))
np.save('advs/opt_attacks_{}_show.npy'.format(idx), advs)
print("Average distortion on random {} images is {}".format(
len(total_distortion), np.mean(total_distortion)))
def stochastic_gradient_loss(model, X, Y, sigma):
mean = np.mean(X)
noise_X = np.random.normal(mean, sigma, X.shape)
def _loss_function(*args):
predictions = model.forward(X, 'train')
noisy_predictions = model.forward(noise_X, 'train')
return model.loss(predictions, Y) + model.loss(noisy_predictions, Y)
gl = _gradient_loss(_loss_function, range(len(model.params)))
parameters = list(model.params.values())
return gl(*parameters)
def discount_rewards(self, rs):
drs = np.zeros_like(rs).asnumpy()
s = 0
for t in reversed(xrange(0, len(rs))):
# Reset the running sum at a game boundary.
if rs[t] != 0:
s = 0
s = s * self.gamma + rs[t]
drs[t] = s
drs -= np.mean(drs)
drs /= np.std(drs)
return drs
def rescale(container, inputs, parameters):
""" recover original distribution at the final layer of every container. """
# returns outputs, factor list
factors = []
all_factors = []
input_shape = inputs.shape[1:]
# find final affine layer
ending = None
for index in range(len(container._modules) - 1, -1, -1):
value = container._modules[index]
if isinstance(value, Affine) or isinstance(value, Convolution):
ending = index
break
# iterate through module
for module_index, module in enumerate(container._modules):
shapes = module.parameter_shape(input_shape)
input_shape = module.output_shape(input_shape)
if isinstance(module, Affine) or isinstance(module, Convolution):
for key, value in shapes.items():
if 'weight' in key:
E_X_2 = np.mean(inputs**2)
if isinstance(module, Affine):
n = value[0]
else:
C, W, H = value[1:]
n = C * W * H
std_from = np.std(parameters[key])
std_to = 1 / (E_X_2 * n)**0.5
rescaling_factor = std_to / std_from
if module_index == ending:
parameters[key] /= np.prod(np.array(factors))
else:
factors.append(rescaling_factor)
parameters[key] *= rescaling_factor
'''
factors.append(rescaling_factor)
parameters[key] *= rescaling_factor
'''
inputs = module.forward(inputs, parameters)
return inputs, factors
def preprocess_data(train, test, validation=None):
"""
Applies zero-centering and normalization of input data for better model performance.
:param train: A numpy matrix containing the data the network is trained on.
:param test: A numpy matrix containing the data used to test the network.
:param validation: An optional numpy matrix containing the data used to apply hyperparameter validation on the network.
:return: The preprocessed matrices.
"""
# Zero-centering (subtracting the mean)
mean = np.mean(train, axis=0) # Using statistic of training set
train -= mean
if validation is not None:
validation -= mean
test -= mean
# Normalization of data dimension to be of equal scale (division by standard deviation)
std = np.std(train, axis=0)
train /= std
if validation is not None:
validation /= std
test /= std
return train, validation, test, mean, std
std = {key: [] for key in model.params}
L_2 = {key: [] for key in model.params}
minimum = {key: [] for key in model.params}
maximum = {key: [] for key in model.params}
for i in range(iterations):
X_batch = data[0][batch_index * batch_size:(batch_index + 1) * batch_size]
Y_batch = data[1][batch_index * batch_size:(batch_index + 1) * batch_size]
batch_index = (batch_index + 1) % batches
gradients, loss = gradient_loss(model, X_batch, Y_batch)
loss = loss.asnumpy()[0]
loss_history.append(loss)
for key, value in zip(model.params.keys(), gradients):
mean[key].append(np.mean(value).asnumpy())
std[key].append(np.std(value).asnumpy())
L_2[key].append(np.mean(value**2).asnumpy())
minimum[key].append(np.min(value).asnumpy())
maximum[key].append(np.max(value).asnumpy())
updater.update(gradients)
if (i + 1) % rescaling_interval == 0:
rescale(mlp, data[2], model.params) # validation data
print 'rescaled'
if (i + 1) % interval == 0:
print 'iteration %d loss %f' % (i + 1, loss)
pickle.dump((loss_history, mean, std, L_2, minimum, maximum),
mat1part = mat[mat1Beg:mat1End, :]
mat2part = mat2[:, mat2Beg:mat2End]
partResult = mnp.dot(mat1part, mat2part)
multiResult[mat1Beg:mat1End, mat2Beg:mat2End] = partResult
print(type(partResult))
# tempResult.append(partResult)
print(partResult)
print(partResult.shape)
# multiResult.append(tempResult)
# multiResult = mnp.block(multiResult)
print(multiResult)
print(multiResult.shape)
print(mnp.sum(multiResult))
print(mnp.mean(multiResult))
# multiResult[mat1Beg:mat1End, mat2Beg:mat2End] = partResult
#
# if j == 9:
# mat1Beg = (i+1)*Dindex1
# mat1End = 64466
# print('mat1:{}:{}'.format(mat1Beg, mat1End))
# mat2Beg = j*Dindex2
# mat2End = (j+1)*Dindex2
# print('mat2:{}:{}'.format(mat2Beg, mat2End))
# mat1part = mat[mat1Beg:mat1End, :]
# mat2part = mat2[:, mat2Beg:mat2End]
# partResult = mnp.dot(mat1part, mat2part)
# multiResult[mat1Beg:mat1End, mat2Beg:mat2End] = partResult
#
xyz_train = (img_name_train.strip('.jpg')).split('_')
x_train = float(xyz_train[0])
y_train = float(xyz_train[1])
z_train = float(xyz_train[2])
labels[flag, 0] = x_train
labels[flag, 1] = y_train
labels[flag, 2] = z_train
labels_x[flag] = x_train
labels_y[flag] = y_train
labels_z[flag] = z_train
flag = flag + 1
img_name_train = image_train_f.readline()
img_name_train = img_name_train.strip('\n')
f_mean = np.mean(features, axis=0)
image_train_f.close()
image_train_list.close()
featuress = np.empty([flag, moments_num], dtype=float)
labelss = np.empty([flag, angle_num], dtype=float)
labelss_x = np.empty([flag, 1], dtype=float)
labelss_y = np.empty([flag, 1], dtype=float)
labelss_z = np.empty([flag, 1], dtype=float)
for i in range(flag):
featuress[i] = features[i]
labelss[i] = labels[i]
labelss_x[i] = labels_x[i]
labelss_y[i] = labels_y[i]
def test_fromnumeric():
# Functions
# 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
# 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
# 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
# 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
# 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',
# 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
# 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
np.take(a, indices)
a = np.array(a)
# a[indices]
np.take(a, [[0, 1], [2, 3]])
a = np.zeros((10, 2))
b = a.T
a = np.arange(6).reshape((3, 2))
np.reshape(a, (2, 3)) # C-like index ordering
np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
a = np.array([[1, 2, 3], [4, 5, 6]])
np.reshape(a, 6)
np.reshape(a, 6, order='F')
np.reshape(a, (3, -1)) # the unspecified value is inferred to be 2
choices = [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23],
[30, 31, 32, 33]]
np.choose([2, 3, 1, 0], choices)
np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
choices = [-10, 10]
np.choose(a, choices)
a = np.array([0, 1]).reshape((2, 1, 1))
c1 = np.array([1, 2, 3]).reshape((1, 3, 1))
c2 = np.array([-1, -2, -3, -4, -5]).reshape((1, 1, 5))
np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
np.repeat(3, 4)
x = np.array([[1, 2], [3, 4]])
np.repeat(x, 2)
np.repeat(x, 3, axis=1)
np.repeat(x, [1, 2], axis=0)
a = np.arange(5)
np.put(a, [0, 2], [-44, -55])
a = np.arange(5)
np.put(a, 22, -5, mode='clip')
x = np.array([[1, 2, 3]])
np.swapaxes(x, 0, 1)
x = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])
np.swapaxes(x, 0, 2)
x = np.arange(4).reshape((2, 2))
np.transpose(x)
x = np.ones((1, 2, 3))
np.transpose(x, (1, 0, 2)).shape
a = np.array([3, 4, 2, 1])
np.partition(a, 3)
np.partition(a, (1, 3))
x = np.array([3, 4, 2, 1])
x[np.argpartition(x, 3)]
x[np.argpartition(x, (1, 3))]
x = [3, 4, 2, 1]
np.array(x)[np.argpartition(x, 3)]
a = np.array([[1, 4], [3, 1]])
np.sort(a) # sort along the last axis
np.sort(a, axis=None) # sort the flattened array
np.sort(a, axis=0) # sort along the first axis
dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ('Galahad', 1.7, 38)]
a = np.array(values, dtype=dtype) # create a structured array
np.sort(a, order='height') # doctest: +SKIP
np.sort(a, order=['age', 'height']) # doctest: +SKIP
x = np.array([3, 1, 2])
np.argsort(x)
x = np.array([[0, 3], [2, 2]])
np.argsort(x, axis=0)
np.argsort(x, axis=1)
x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
np.argsort(x, order=('x', 'y'))
np.argsort(x, order=('y', 'x'))
a = np.arange(6).reshape(2, 3)
np.argmax(a)
np.argmax(a, axis=0)
np.argmax(a, axis=1)
b = np.arange(6)
b[1] = 5
np.argmax(b) # Only the first occurrence is returned.
a = np.arange(6).reshape(2, 3)
np.argmin(a)
np.argmin(a, axis=0)
np.argmin(a, axis=1)
b = np.arange(6)
b[4] = 0
np.argmin(b) # Only the first occurrence is returned.
np.searchsorted([1, 2, 3, 4, 5], 3)
np.searchsorted([1, 2, 3, 4, 5], 3, side='right')
np.searchsorted([1, 2, 3, 4, 5], [-10, 10, 2, 3])
a = np.array([[0, 1], [2, 3]])
np.resize(a, (2, 3))
np.resize(a, (1, 4))
np.resize(a, (2, 4))
x = np.array([[[0], [1], [2]]])
x.shape
np.squeeze(x).shape
np.squeeze(x, axis=(2, )).shape
a = np.arange(4).reshape(2, 2)
a = np.arange(8).reshape(2, 2, 2)
a
a[:, :, 0] # main diagonal is [0 6]
a[:, :, 1] # main diagonal is [1 7]
np.trace(np.eye(3))
a = np.arange(8).reshape((2, 2, 2))
np.trace(a)
a = np.arange(24).reshape((2, 2, 2, 3))
np.trace(a).shape
x = np.array([[1, 2, 3], [4, 5, 6]])
np.ravel(x)
x.reshape(-1)
np.ravel(x, order='F')
np.ravel(x.T)
np.ravel(x.T, order='A')
a = np.arange(3)[::-1]
a
# a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
x = np.eye(3)
np.nonzero(x)
x[np.nonzero(x)]
np.transpose(np.nonzero(x))
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a > 3
np.nonzero(a > 3)
np.shape(np.eye(3))
np.shape([[1, 2]])
np.shape([0])
np.shape(0)
a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
np.shape(a)
a.shape
a = np.array([[1, 2], [3, 4], [5, 6]])
np.compress([0, 1], a, axis=0)
np.compress([False, True, True], a, axis=0)
np.compress([False, True], a, axis=1)
np.compress([False, True], a)
a = np.arange(10)
np.clip(a, 1, 8)
np.clip(a, 3, 6, out=a)
a = np.arange(10)
np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
np.sum([])
np.sum([0.5, 1.5])
np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
np.sum([[0, 1], [0, 5]])
np.sum([[0, 1], [0, 5]], axis=0)
np.sum([[0, 1], [0, 5]], axis=1)
# np.ones(128, dtype=np.int8).sum(dtype=np.int8)
# np.any([[True, False], [True, True]])
# np.any([[True, False], [False, False]], axis=0)
# np.any([-1, 0, 5])
# np.any(np.nan)
# np.all([[True,False],[True,True]])
# np.all([[True,False],[True,True]], axis=0)
# np.all([-1, 4, 5])
# np.all([1.0, np.nan])
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumsum(a)
np.cumsum(a, dtype=float) # specifies type of output value(s)
np.cumsum(a, axis=0) # sum over rows for each of the 3 columns
np.cumsum(a, axis=1) # sum over columns for each of the 2 rows
x = np.arange(4).reshape((2, 2))
np.ptp(x, axis=0)
np.ptp(x, axis=1)
a = np.arange(4).reshape((2, 2))
np.amax(a) # Maximum of the flattened array
np.amax(a, axis=0) # Maxima along the first axis
np.amax(a, axis=1) # Maxima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amax(b)
np.nanmax(b)
a = np.arange(4).reshape((2, 2))
np.amin(a) # Minimum of the flattened array
np.amin(a, axis=0) # Minima along the first axis
np.amin(a, axis=1) # Minima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amin(b)
np.nanmin(b)
a = np.zeros((7, 4, 5))
a.shape[0]
np.alen(a)
x = np.array([536870910, 536870910, 536870910, 536870910])
np.prod(x) #random
np.prod([])
np.prod([1., 2.])
np.prod([[1., 2.], [3., 4.]])
np.prod([[1., 2.], [3., 4.]], axis=1)
x = np.array([1, 2, 3], dtype=np.uint8)
# np.prod(x).dtype == np.uint
x = np.array([1, 2, 3], dtype=np.int8)
# np.prod(x).dtype == np.int
a = np.array([1, 2, 3])
np.cumprod(a) # intermediate results 1, 1*2
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumprod(a, dtype=float) # specify type of output
np.cumprod(a, axis=0)
np.cumprod(a, axis=1)
np.ndim([[1, 2, 3], [4, 5, 6]])
np.ndim(np.array([[1, 2, 3], [4, 5, 6]]))
np.ndim(1)
a = np.array([[1, 2, 3], [4, 5, 6]])
np.size(a)
np.size(a, 1)
np.size(a, 0)
np.around([0.37, 1.64])
np.around([0.37, 1.64], decimals=1)
np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned
np.around([1, 2, 3, 11], decimals=-1)
a = np.array([[1, 2], [3, 4]])
np.mean(a)
np.mean(a, axis=0)
np.mean(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.mean(a)
np.mean(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.std(a)
np.std(a, axis=0)
np.std(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.std(a)
np.std(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.var(a)
np.var(a, axis=0)
np.var(a, axis=1)
a = np.zeros((2, 512 * 512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.var(a)
np.var(a, dtype=np.float64)
def frac_err(W_vect, X, T):
return np.mean(np.argmax(T, axis=1) != np.argmax(pred_fun(W_vect, X), axis=1))
def barrier(S0, K, B, tau, r, q, v, M, N):
S = pricepaths(S0, tau, r, q, v, M, N)
l = np.min(S, 0) > B
payoffs = l * np.maximum(S[-1, :] - K, 0)
return math.exp(-r * tau) * np.mean(payoffs)
def main():
import argparse, random
random.seed(0)
_arg_parser = argparse.ArgumentParser()
_arg_parser.add_argument('--vocab',
default='s_lstm_deparser_data/vocab.txt',
type=str,
action='store',
help='')
_arg_parser.add_argument(
'--train',
default='s_lstm_deparser_data/small-train.unk.txt',
type=str,
action='store',
help='')
_arg_parser.add_argument('--dev',
default='s_lstm_deparser_data/small-dev.unk.txt',
type=str,
action='store',
help='')
_arg_parser.add_argument('--emb_size',
default=64,
type=int,
action='store',
help='')
_arg_parser.add_argument('--rnn_size',
default=64,
type=int,
action='store',
help='')
_args = _arg_parser.parse_args()
# load training and dev data
vocab_words = Vocab.from_file(_args.vocab)
train = list(read_oracle(_args.train, vocab_words, vocab_acts))
dev = list(read_oracle(_args.dev, vocab_words, vocab_acts))
tp = TransitionParser(vocab_words, _args.emb_size, _args.rnn_size,
NUM_ACTIONS)
solver = MySolver(model=tp, update_rule='adam')
solver.init()
validation_losses = []
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.DEBUG)
for epoch in range(5):
random.shuffle(train)
words = 0
total_loss = 0.0
for i, (s, a) in enumerate(train):
instances_processed = float(i)
# periodically report validation loss
e = instances_processed / len(train)
if instances_processed % 1000 == 0:
dev_words = 0
dev_loss = 0.0
for (ds, da) in dev:
dev_words += len(ds)
dev_loss += np.mean(tp.parse(ds, da))
# print 'sent:', ds
# print 'oracle:', da
# exit()
print('[validation] epoch {}: per-word loss: {}'.format(
e, dev_loss / dev_words))
validation_losses.append(dev_loss)
# report training loss
if instances_processed % 100 == 0 and words > 0:
print('epoch {}: per-word loss: {}'.format(
e, total_loss / words))
words = 0
total_loss = 0.0
# here we do training
total_loss += np.mean(
solver.train_on_batch(s, a)
) # returns None for 1-word sentencs (it's clear how to parse them)
words += len(s)
def test_fromnumeric():
# Functions
# 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
# 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
# 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
# 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
# 'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',
# 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
# 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
np.take(a, indices)
a = np.array(a)
# a[indices]
np.take(a, [[0, 1], [2, 3]])
a = np.zeros((10, 2))
b = a.T
a = np.arange(6).reshape((3, 2))
np.reshape(a, (2, 3)) # C-like index ordering
np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
a = np.array([[1,2,3], [4,5,6]])
np.reshape(a, 6)
np.reshape(a, 6, order='F')
np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
choices = [[0, 1, 2, 3], [10, 11, 12, 13],
[20, 21, 22, 23], [30, 31, 32, 33]]
np.choose([2, 3, 1, 0], choices)
np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
choices = [-10, 10]
np.choose(a, choices)
a = np.array([0, 1]).reshape((2,1,1))
c1 = np.array([1, 2, 3]).reshape((1,3,1))
c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
np.repeat(3, 4)
x = np.array([[1,2],[3,4]])
np.repeat(x, 2)
np.repeat(x, 3, axis=1)
np.repeat(x, [1, 2], axis=0)
a = np.arange(5)
np.put(a, [0, 2], [-44, -55])
a = np.arange(5)
np.put(a, 22, -5, mode='clip')
x = np.array([[1,2,3]])
np.swapaxes(x,0,1)
x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
np.swapaxes(x,0,2)
x = np.arange(4).reshape((2,2))
np.transpose(x)
x = np.ones((1, 2, 3))
np.transpose(x, (1, 0, 2)).shape
a = np.array([3, 4, 2, 1])
np.partition(a, 3)
np.partition(a, (1, 3))
x = np.array([3, 4, 2, 1])
x[np.argpartition(x, 3)]
x[np.argpartition(x, (1, 3))]
x = [3, 4, 2, 1]
np.array(x)[np.argpartition(x, 3)]
a = np.array([[1,4],[3,1]])
np.sort(a) # sort along the last axis
np.sort(a, axis=None) # sort the flattened array
np.sort(a, axis=0) # sort along the first axis
dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
('Galahad', 1.7, 38)]
a = np.array(values, dtype=dtype) # create a structured array
np.sort(a, order='height') # doctest: +SKIP
np.sort(a, order=['age', 'height']) # doctest: +SKIP
x = np.array([3, 1, 2])
np.argsort(x)
x = np.array([[0, 3], [2, 2]])
np.argsort(x, axis=0)
np.argsort(x, axis=1)
x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
np.argsort(x, order=('x','y'))
np.argsort(x, order=('y','x'))
a = np.arange(6).reshape(2,3)
np.argmax(a)
np.argmax(a, axis=0)
np.argmax(a, axis=1)
b = np.arange(6)
b[1] = 5
np.argmax(b) # Only the first occurrence is returned.
a = np.arange(6).reshape(2,3)
np.argmin(a)
np.argmin(a, axis=0)
np.argmin(a, axis=1)
b = np.arange(6)
b[4] = 0
np.argmin(b) # Only the first occurrence is returned.
np.searchsorted([1,2,3,4,5], 3)
np.searchsorted([1,2,3,4,5], 3, side='right')
np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
a=np.array([[0,1],[2,3]])
np.resize(a,(2,3))
np.resize(a,(1,4))
np.resize(a,(2,4))
x = np.array([[[0], [1], [2]]])
x.shape
np.squeeze(x).shape
np.squeeze(x, axis=(2,)).shape
a = np.arange(4).reshape(2,2)
a = np.arange(8).reshape(2,2,2); a
a[:,:,0] # main diagonal is [0 6]
a[:,:,1] # main diagonal is [1 7]
np.trace(np.eye(3))
a = np.arange(8).reshape((2,2,2))
np.trace(a)
a = np.arange(24).reshape((2,2,2,3))
np.trace(a).shape
x = np.array([[1, 2, 3], [4, 5, 6]])
np.ravel(x)
x.reshape(-1)
np.ravel(x, order='F')
np.ravel(x.T)
np.ravel(x.T, order='A')
a = np.arange(3)[::-1]; a
# a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
x = np.eye(3)
np.nonzero(x)
x[np.nonzero(x)]
np.transpose(np.nonzero(x))
a = np.array([[1,2,3],[4,5,6],[7,8,9]])
a > 3
np.nonzero(a > 3)
np.shape(np.eye(3))
np.shape([[1, 2]])
np.shape([0])
np.shape(0)
a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
np.shape(a)
a.shape
a = np.array([[1, 2], [3, 4], [5, 6]])
np.compress([0, 1], a, axis=0)
np.compress([False, True, True], a, axis=0)
np.compress([False, True], a, axis=1)
np.compress([False, True], a)
a = np.arange(10)
np.clip(a, 1, 8)
np.clip(a, 3, 6, out=a)
a = np.arange(10)
np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8)
np.sum([])
np.sum([0.5, 1.5])
np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
np.sum([[0, 1], [0, 5]])
np.sum([[0, 1], [0, 5]], axis=0)
np.sum([[0, 1], [0, 5]], axis=1)
# np.ones(128, dtype=np.int8).sum(dtype=np.int8)
# np.any([[True, False], [True, True]])
# np.any([[True, False], [False, False]], axis=0)
# np.any([-1, 0, 5])
# np.any(np.nan)
# np.all([[True,False],[True,True]])
# np.all([[True,False],[True,True]], axis=0)
# np.all([-1, 4, 5])
# np.all([1.0, np.nan])
a = np.array([[1,2,3], [4,5,6]])
np.cumsum(a)
np.cumsum(a, dtype=float) # specifies type of output value(s)
np.cumsum(a,axis=0) # sum over rows for each of the 3 columns
np.cumsum(a,axis=1) # sum over columns for each of the 2 rows
x = np.arange(4).reshape((2,2))
np.ptp(x, axis=0)
np.ptp(x, axis=1)
a = np.arange(4).reshape((2,2))
np.amax(a) # Maximum of the flattened array
np.amax(a, axis=0) # Maxima along the first axis
np.amax(a, axis=1) # Maxima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amax(b)
np.nanmax(b)
a = np.arange(4).reshape((2,2))
np.amin(a) # Minimum of the flattened array
np.amin(a, axis=0) # Minima along the first axis
np.amin(a, axis=1) # Minima along the second axis
b = np.arange(5, dtype=np.float)
# b[2] = np.NaN
np.amin(b)
np.nanmin(b)
a = np.zeros((7,4,5))
a.shape[0]
np.alen(a)
x = np.array([536870910, 536870910, 536870910, 536870910])
np.prod(x) #random
np.prod([])
np.prod([1.,2.])
np.prod([[1.,2.],[3.,4.]])
np.prod([[1.,2.],[3.,4.]], axis=1)
x = np.array([1, 2, 3], dtype=np.uint8)
# np.prod(x).dtype == np.uint
x = np.array([1, 2, 3], dtype=np.int8)
# np.prod(x).dtype == np.int
a = np.array([1,2,3])
np.cumprod(a) # intermediate results 1, 1*2
a = np.array([[1, 2, 3], [4, 5, 6]])
np.cumprod(a, dtype=float) # specify type of output
np.cumprod(a, axis=0)
np.cumprod(a,axis=1)
np.ndim([[1,2,3],[4,5,6]])
np.ndim(np.array([[1,2,3],[4,5,6]]))
np.ndim(1)
a = np.array([[1,2,3],[4,5,6]])
np.size(a)
np.size(a,1)
np.size(a,0)
np.around([0.37, 1.64])
np.around([0.37, 1.64], decimals=1)
np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
np.around([1,2,3,11], decimals=-1)
a = np.array([[1, 2], [3, 4]])
np.mean(a)
np.mean(a, axis=0)
np.mean(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.mean(a)
np.mean(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.std(a)
np.std(a, axis=0)
np.std(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.std(a)
np.std(a, dtype=np.float64)
a = np.array([[1, 2], [3, 4]])
np.var(a)
np.var(a, axis=0)
np.var(a, axis=1)
a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1
np.var(a)
np.var(a, dtype=np.float64)
def dU(beta):
return mp.dot(X.T, (mp.exp(mp.dot(X,beta))/(1+mp.exp(mp.dot(X,beta))) - y)) + beta/alpha
D = X.shape[1]
q = mp.zeros((D, 1), dtype=mp.float32)
out = mp.zeros((n_iter, D), dtype=mp.float32)
for i in range(n_iter):
q = hmc(U, dU, epsilon, L, q)
out[i,:] = mp.ravel(q)
return out
with cpu() if args.mode == 'cpu' else gpu(0):
with open('params.json') as params_file:
out = {}
params = json.load(params_file)
X_train, y_train, X_test, y_test = get_data()
X_train = mp.array(X_train)
y_train = mp.array(y_train)
X_test = mp.array(X_test)
y_test = mp.array(y_test)
y_train = mp.expand_dims(y_train, 1)
z = lr_hmc(y_train, X_train, params['epsilon'], params['n_leaps'], params['alpha'], 1) # Warm-up
t = time.perf_counter()
z = lr_hmc(y_train, X_train, params['epsilon'], params['n_leaps'], params['alpha'], params['n_iter'])
t = time.perf_counter() - t
out[f'minpy-{args.mode}'] = t
coef_ = mp.mean(z[params['burn_in']:], 0)
acc = mp.mean((sigmoid(mp.dot(X_test, coef_)) > 0.5) == y_test)[0]
assert acc > 0.8
print(json.dumps(out))
def infer_std(inputs, weight_shape):
import minpy.numpy as np
E_X_2 = np.mean(inputs**2)
return 1 / (E_X_2 * float(weight_shape[0]))**0.5
def garchSim(ret2, p):
h = np.zeros(ret2.shape[0], dtype='float32')
h[0] = np.mean(ret2)
for i in range(1, ret2.shape[0]):
h[i] = p[0] + p[1] * ret2[i - 1] + p[2] * h[i - 1]
return h
from facility import *
from solver_primitives import *
HIDDEN_LAYERS = 4
shapes = (1024, ) * HIDDEN_LAYERS + (10, )
storage = {}
activation = builder.ReLU
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Affine(shapes[-1]))
mlp.append(builder.Export('affine%d' % (len(shapes) - 1), storage))
model = builder.Model(mlp, 'softmax', (3072, ))
initialize(model)
X = np.random.normal(0, 1, (64, 3072))
output = model.forward(X, 'train')
print 'origin'
for key, value in storage.items():
print key, np.std(value)
rescale(mlp, X, model.params)
rescaled_output = model.forward(X, 'train')
print 'rescaled'
for key, value in storage.items():
print key, np.std(value)
print np.mean(np.abs(output - rescaled_output))
