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Example 1
def update(self,frame,events):
falloff = self.falloff
img = frame.img
pts = [denormalize(pt['norm_pos'],frame.img.shape[:-1][::-1],flip_y=True) for pt in events.get('gaze_positions',[]) if pt['confidence']>=self.g_pool.min_data_confidence]
overlay = np.ones(img.shape[:-1],dtype=img.dtype)
# draw recent gaze postions as black dots on an overlay image.
for gaze_point in pts:
try:
overlay[int(gaze_point[1]),int(gaze_point[0])] = 0
except:
pass
out = cv2.distanceTransform(overlay,cv2.DIST_L2, 5)
# fix for opencv binding inconsitency
if type(out)==tuple:
out = out[0]
overlay = 1/(out/falloff+1)
img[:] = np.multiply(img, cv2.cvtColor(overlay,cv2.COLOR_GRAY2RGB), casting="unsafe") Example 2
def svgd_kernel(self, h = -1):
sq_dist = pdist(self.theta)
pairwise_dists = squareform(sq_dist)**2
if h < 0: # if h < 0, using median trick
h = np.median(pairwise_dists)
h = np.sqrt(0.5 * h / np.log(self.theta.shape[0]+1))
# compute the rbf kernel
Kxy = np.exp( -pairwise_dists / h**2 / 2)
dxkxy = -np.matmul(Kxy, self.theta)
sumkxy = np.sum(Kxy, axis=1)
for i in range(self.theta.shape[1]):
dxkxy[:, i] = dxkxy[:,i] + np.multiply(self.theta[:,i],sumkxy)
dxkxy = dxkxy / (h**2)
return (Kxy, dxkxy) Example 3
def EStep(self):
P = np.zeros((self.M, self.N))
for i in range(0, self.M):
diff = self.X - np.tile(self.TY[i, :], (self.N, 1))
diff = np.multiply(diff, diff)
P[i, :] = P[i, :] + np.sum(diff, axis=1)
c = (2 * np.pi * self.sigma2) ** (self.D / 2)
c = c * self.w / (1 - self.w)
c = c * self.M / self.N
P = np.exp(-P / (2 * self.sigma2))
den = np.sum(P, axis=0)
den = np.tile(den, (self.M, 1))
den[den==0] = np.finfo(float).eps
self.P = np.divide(P, den)
self.Pt1 = np.sum(self.P, axis=0)
self.P1 = np.sum(self.P, axis=1)
self.Np = np.sum(self.P1) Example 4
def derivative(self, input=None):
"""The derivative of sigmoid is
.. math:: \\frac{dy}{dx} & = (1-\\varphi(x)) \\otimes \\varphi(x) \\\\
& = \\frac{e^{-x}}{(1+e^{-x})^2} \\\\
& = \\frac{e^x}{(1+e^x)^2}
Returns
-------
float32
The derivative of sigmoid function.
"""
last_forward = self.forward(input) if input else self.last_forward
return np.multiply(last_forward, 1 - last_forward)
# sigmoid-end
# tanh-start Example 5
def svgd_kernel(self, theta, h = -1):
sq_dist = pdist(theta)
pairwise_dists = squareform(sq_dist)**2
if h < 0: # if h < 0, using median trick
h = np.median(pairwise_dists)
h = np.sqrt(0.5 * h / np.log(theta.shape[0]+1))
# compute the rbf kernel
Kxy = np.exp( -pairwise_dists / h**2 / 2)
dxkxy = -np.matmul(Kxy, theta)
sumkxy = np.sum(Kxy, axis=1)
for i in range(theta.shape[1]):
dxkxy[:, i] = dxkxy[:,i] + np.multiply(theta[:,i],sumkxy)
dxkxy = dxkxy / (h**2)
return (Kxy, dxkxy) Example 6
def gradientDescent(X, y, theta, alpha, iters):
temp = np.matrix(np.zeros(theta.shape))
params = int(theta.ravel().shape[1]) #flattens
cost = np.zeros(iters)
for i in range(iters):
err = (X * theta.T) - y
for j in range(params):
term = np.multiply(err, X[:,j])
temp[0, j] = theta[0, j] - ((alpha / len(X)) * np.sum(term))
theta = temp
cost[i] = computeCost(X, y, theta)
return theta, cost Example 7
def computeCost(X, y, theta):
inner = np.power(((X * theta.T) - y), 2)
return np.sum(inner) / (2 * len(X))
#def gradientDescent(X, y, theta, alpha, iters):
# temp = np.matrix(np.zeros(theta.shape))
# params = int(theta.ravel().shape[1]) #flattens
# cost = np.zeros(iters)
#
# for i in range(iters):
# err = (X * theta.T) - y
#
# for j in range(params):
# term = np.multiply(err, X[:,j])
# temp[0, j] = theta[0, j] - ((alpha / len(X)) * np.sum(term))
#
# theta = temp
# cost[i] = computeCost(X, y, theta)
#
# return theta, cost Example 8
def _extract_images(filename):
"""???????????????????
:param filename: ?????
:return: 4??numpy??[index, y, x, depth]? ???np.float32
"""
images = []
print('Extracting {}'.format(filename))
with gzip.GzipFile(fileobj=open(filename, 'rb')) as f:
buf = f.read()
index = 0
magic, num_images, rows, cols = struct.unpack_from('>IIII', buf, index)
if magic != 2051:
raise ValueError('Invalid magic number {} in MNIST image file: {}'.format(magic, filename))
index += struct.calcsize('>IIII')
for i in range(num_images):
img = struct.unpack_from('>784B', buf, index)
index += struct.calcsize('>784B')
img = np.array(img, dtype=np.float32)
# ????[0,255]???[0,1]
img = np.multiply(img, 1.0 / 255.0)
img = img.reshape(rows, cols, 1)
images.append(img)
return np.array(images, dtype=np.float32) Example 9
def get_max_q_values(
self,
next_states: np.ndarray,
possible_next_actions: Optional[np.ndarray] = None,
use_target_network: Optional[bool] = True
) -> np.ndarray:
q_values = self.get_q_values_all_actions(
next_states, use_target_network
)
if possible_next_actions is not None:
mask = np.multiply(
np.logical_not(possible_next_actions),
self.ACTION_NOT_POSSIBLE_VAL
)
q_values += mask
return np.max(q_values, axis=1, keepdims=True) Example 10
def gen_training_data(
num_features,
num_training_samples,
num_outputs,
noise_scale=0.1,
):
np.random.seed(0)
random.seed(1)
input_distribution = stats.norm()
training_inputs = input_distribution.rvs(
size=(num_training_samples, num_features)
).astype(np.float32)
weights = np.random.normal(size=(num_outputs, num_features)
).astype(np.float32).transpose()
noise = np.multiply(
np.random.normal(size=(num_training_samples, num_outputs)), noise_scale
)
training_outputs = (np.dot(training_inputs, weights) +
noise).astype(np.float32)
return training_inputs, training_outputs, weights, input_distribution Example 11
def make_tfidf(arr):
'''input, numpy array with flavor counts for each recipe and compounds
return numpy array adjusted as tfidf
'''
arr2 = arr.copy()
N=arr2.shape[0]
l2_rows = np.sqrt(np.sum(arr2**2, axis=1)).reshape(N, 1)
l2_rows[l2_rows==0]=1
arr2_norm = arr2/l2_rows
arr2_freq = np.sum(arr2_norm>0, axis=0)
arr2_idf = np.log(float(N+1) / (1.0 + arr2_freq)) + 1.0
from sklearn.preprocessing import normalize
tfidf = np.multiply(arr2_norm, arr2_idf)
tfidf = normalize(tfidf, norm='l2', axis=1)
print tfidf.shape
return tfidf Example 12
def make_tfidf(arr):
'''input, numpy array with flavor counts for each recipe and compounds
return numpy array adjusted as tfidf
'''
arr2 = arr.copy()
N=arr2.shape[0]
l2_rows = np.sqrt(np.sum(arr2**2, axis=1)).reshape(N, 1)
l2_rows[l2_rows==0]=1
arr2_norm = arr2/l2_rows
arr2_freq = np.sum(arr2_norm>0, axis=0)
arr2_idf = np.log(float(N+1) / (1.0 + arr2_freq)) + 1.0
from sklearn.preprocessing import normalize
tfidf = np.multiply(arr2_norm, arr2_idf)
tfidf = normalize(tfidf, norm='l2', axis=1)
print tfidf.shape
return tfidf Example 13
def train(self, training_data_array):
for data in training_data_array:
# ??????????
y1 = np.dot(np.mat(self.theta1), np.mat(data.y0).T)
sum1 = y1 + np.mat(self.input_layer_bias)
y1 = self.sigmoid(sum1)
y2 = np.dot(np.array(self.theta2), y1)
y2 = np.add(y2, self.hidden_layer_bias)
y2 = self.sigmoid(y2)
# ??????????
actual_vals = [0] * 10
actual_vals[data.label] = 1
output_errors = np.mat(actual_vals).T - np.mat(y2)
hidden_errors = np.multiply(np.dot(np.mat(self.theta2).T, output_errors), self.sigmoid_prime(sum1))
# ???????????
self.theta1 += self.LEARNING_RATE * np.dot(np.mat(hidden_errors), np.mat(data.y0))
self.theta2 += self.LEARNING_RATE * np.dot(np.mat(output_errors), np.mat(y1).T)
self.hidden_layer_bias += self.LEARNING_RATE * output_errors
self.input_layer_bias += self.LEARNING_RATE * hidden_errors Example 14
def ct2lg(dX, dY, dZ, lat, lon):
n = dX.size
R = np.zeros((3, 3, n))
R[0, 0, :] = -np.multiply(np.sin(np.deg2rad(lat)), np.cos(np.deg2rad(lon)))
R[0, 1, :] = -np.multiply(np.sin(np.deg2rad(lat)), np.sin(np.deg2rad(lon)))
R[0, 2, :] = np.cos(np.deg2rad(lat))
R[1, 0, :] = -np.sin(np.deg2rad(lon))
R[1, 1, :] = np.cos(np.deg2rad(lon))
R[1, 2, :] = np.zeros((1, n))
R[2, 0, :] = np.multiply(np.cos(np.deg2rad(lat)), np.cos(np.deg2rad(lon)))
R[2, 1, :] = np.multiply(np.cos(np.deg2rad(lat)), np.sin(np.deg2rad(lon)))
R[2, 2, :] = np.sin(np.deg2rad(lat))
dxdydz = np.column_stack((np.column_stack((dX, dY)), dZ))
RR = np.reshape(R[0, :, :], (3, n))
dx = np.sum(np.multiply(RR, dxdydz.transpose()), axis=0)
RR = np.reshape(R[1, :, :], (3, n))
dy = np.sum(np.multiply(RR, dxdydz.transpose()), axis=0)
RR = np.reshape(R[2, :, :], (3, n))
dz = np.sum(np.multiply(RR, dxdydz.transpose()), axis=0)
return dx, dy, dz Example 15
def ct2lg(self, dX, dY, dZ, lat, lon):
n = dX.size
R = numpy.zeros((3, 3, n))
R[0, 0, :] = -numpy.multiply(numpy.sin(numpy.deg2rad(lat)), numpy.cos(numpy.deg2rad(lon)))
R[0, 1, :] = -numpy.multiply(numpy.sin(numpy.deg2rad(lat)), numpy.sin(numpy.deg2rad(lon)))
R[0, 2, :] = numpy.cos(numpy.deg2rad(lat))
R[1, 0, :] = -numpy.sin(numpy.deg2rad(lon))
R[1, 1, :] = numpy.cos(numpy.deg2rad(lon))
R[1, 2, :] = numpy.zeros((1, n))
R[2, 0, :] = numpy.multiply(numpy.cos(numpy.deg2rad(lat)), numpy.cos(numpy.deg2rad(lon)))
R[2, 1, :] = numpy.multiply(numpy.cos(numpy.deg2rad(lat)), numpy.sin(numpy.deg2rad(lon)))
R[2, 2, :] = numpy.sin(numpy.deg2rad(lat))
dxdydz = numpy.column_stack((numpy.column_stack((dX, dY)), dZ))
RR = numpy.reshape(R[0, :, :], (3, n))
dx = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
RR = numpy.reshape(R[1, :, :], (3, n))
dy = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
RR = numpy.reshape(R[2, :, :], (3, n))
dz = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
return dx, dy, dz Example 16
def ct2lg(dX, dY, dZ, lat, lon):
n = dX.size
R = np.zeros((3, 3, n))
R[0, 0, :] = -np.multiply(np.sin(np.deg2rad(lat)), np.cos(np.deg2rad(lon)))
R[0, 1, :] = -np.multiply(np.sin(np.deg2rad(lat)), np.sin(np.deg2rad(lon)))
R[0, 2, :] = np.cos(np.deg2rad(lat))
R[1, 0, :] = -np.sin(np.deg2rad(lon))
R[1, 1, :] = np.cos(np.deg2rad(lon))
R[1, 2, :] = np.zeros((1, n))
R[2, 0, :] = np.multiply(np.cos(np.deg2rad(lat)), np.cos(np.deg2rad(lon)))
R[2, 1, :] = np.multiply(np.cos(np.deg2rad(lat)), np.sin(np.deg2rad(lon)))
R[2, 2, :] = np.sin(np.deg2rad(lat))
dxdydz = np.column_stack((np.column_stack((dX, dY)), dZ))
RR = np.reshape(R[0, :, :], (3, n))
dx = np.sum(np.multiply(RR, dxdydz.transpose()), axis=0)
RR = np.reshape(R[1, :, :], (3, n))
dy = np.sum(np.multiply(RR, dxdydz.transpose()), axis=0)
RR = np.reshape(R[2, :, :], (3, n))
dz = np.sum(np.multiply(RR, dxdydz.transpose()), axis=0)
return dx, dy, dz Example 17
def ct2lg(self, dX, dY, dZ, lat, lon):
n = dX.size
R = numpy.zeros((3, 3, n))
R[0, 0, :] = -numpy.multiply(numpy.sin(numpy.deg2rad(lat)), numpy.cos(numpy.deg2rad(lon)))
R[0, 1, :] = -numpy.multiply(numpy.sin(numpy.deg2rad(lat)), numpy.sin(numpy.deg2rad(lon)))
R[0, 2, :] = numpy.cos(numpy.deg2rad(lat))
R[1, 0, :] = -numpy.sin(numpy.deg2rad(lon))
R[1, 1, :] = numpy.cos(numpy.deg2rad(lon))
R[1, 2, :] = numpy.zeros((1, n))
R[2, 0, :] = numpy.multiply(numpy.cos(numpy.deg2rad(lat)), numpy.cos(numpy.deg2rad(lon)))
R[2, 1, :] = numpy.multiply(numpy.cos(numpy.deg2rad(lat)), numpy.sin(numpy.deg2rad(lon)))
R[2, 2, :] = numpy.sin(numpy.deg2rad(lat))
dxdydz = numpy.column_stack((numpy.column_stack((dX, dY)), dZ))
RR = numpy.reshape(R[0, :, :], (3, n))
dx = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
RR = numpy.reshape(R[1, :, :], (3, n))
dy = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
RR = numpy.reshape(R[2, :, :], (3, n))
dz = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
return dx, dy, dz Example 18
def ct2lg(self, dX, dY, dZ, lat, lon):
n = dX.size
R = numpy.zeros((3, 3, n))
R[0, 0, :] = -numpy.multiply(numpy.sin(numpy.deg2rad(lat)), numpy.cos(numpy.deg2rad(lon)))
R[0, 1, :] = -numpy.multiply(numpy.sin(numpy.deg2rad(lat)), numpy.sin(numpy.deg2rad(lon)))
R[0, 2, :] = numpy.cos(numpy.deg2rad(lat))
R[1, 0, :] = -numpy.sin(numpy.deg2rad(lon))
R[1, 1, :] = numpy.cos(numpy.deg2rad(lon))
R[1, 2, :] = numpy.zeros((1, n))
R[2, 0, :] = numpy.multiply(numpy.cos(numpy.deg2rad(lat)), numpy.cos(numpy.deg2rad(lon)))
R[2, 1, :] = numpy.multiply(numpy.cos(numpy.deg2rad(lat)), numpy.sin(numpy.deg2rad(lon)))
R[2, 2, :] = numpy.sin(numpy.deg2rad(lat))
dxdydz = numpy.column_stack((numpy.column_stack((dX, dY)), dZ))
RR = numpy.reshape(R[0, :, :], (3, n))
dx = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
RR = numpy.reshape(R[1, :, :], (3, n))
dy = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
RR = numpy.reshape(R[2, :, :], (3, n))
dz = numpy.sum(numpy.multiply(RR, dxdydz.transpose()), axis=0)
return dx, dy, dz Example 19
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 20
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 21
def phaseSensitive(self):
"""
Computation of Phase Sensitive Mask. As appears in :
H Erdogan, John R. Hershey, Shinji Watanabe, and Jonathan Le Roux,
"Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks,"
in ICASSP 2015, Brisbane, April, 2015.
Args:
mTarget: (2D ndarray) Magnitude Spectrogram of the target component
pTarget: (2D ndarray) Phase Spectrogram of the output component
mY: (2D ndarray) Magnitude Spectrogram of the residual component
pY: (2D ndarray) Phase Spectrogram of the residual component
Returns:
mask: (2D ndarray) Array that contains time frequency gain values
"""
print('Phase Sensitive Masking.')
# Compute Phase Difference
Theta = (self._pTarget - self._pY)
self._mask = 2./ (1. + np.exp(-np.multiply(np.divide(self._sTarget, self._eps + self._nResidual), np.cos(Theta)))) - 1. Example 22
def __init__(self,
images,
labels,
dtype=dtypes.float32,
reshape=True):
dtype = dtypes.as_dtype(dtype).base_dtype
if dtype not in (dtypes.uint8, dtypes.float32):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %dtype)
self._num_examples = images.shape[0]
if dtype == dtypes.float32:
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(np.float32)
images = np.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 23
def test_wrap_with_iterable(self):
# test fix for bug #1026:
class with_wrap(np.ndarray):
__array_priority__ = 10
def __new__(cls):
return np.asarray(1).view(cls).copy()
def __array_wrap__(self, arr, context):
return arr.view(type(self))
a = with_wrap()
x = ncu.multiply(a, (1, 2, 3))
self.assertTrue(isinstance(x, with_wrap))
assert_array_equal(x, np.array((1, 2, 3))) Example 24
def test_out_override(self):
# 2016-01-29: NUMPY_UFUNC_DISABLED
return
# regression test for github bug 4753
class OutClass(np.ndarray):
def __numpy_ufunc__(self, ufunc, method, i, inputs, **kw):
if 'out' in kw:
tmp_kw = kw.copy()
tmp_kw.pop('out')
func = getattr(ufunc, method)
kw['out'][...] = func(*inputs, **tmp_kw)
A = np.array([0]).view(OutClass)
B = np.array([5])
C = np.array([6])
np.multiply(C, B, A)
assert_equal(A[0], 30)
assert_(isinstance(A, OutClass))
A[0] = 0
np.multiply(C, B, out=A)
assert_equal(A[0], 30)
assert_(isinstance(A, OutClass)) Example 25
def predictions_for_tiles(test_images, model):
"""Batch predictions on the test image set, to avoid a memory spike."""
npy_test_images = numpy.array([img_loc_tuple[0] for img_loc_tuple in test_images])
test_images = npy_test_images.astype(numpy.float32)
test_images = numpy.multiply(test_images, 1.0 / 255.0)
all_predictions = []
for x in range(0, len(test_images) - 100, 100):
for p in model.predict(test_images[x:x + 100]):
all_predictions.append(p)
for p in model.predict(test_images[len(all_predictions):]):
all_predictions.append(p)
assert len(all_predictions) == len(test_images)
return all_predictions Example 26
def knn_masked_data(trX,trY,missing_data_dir, input_shape, k):
raw_im_data = np.loadtxt(join(script_dir,missing_data_dir,'index.txt'),delimiter=' ',dtype=str)
raw_mask_data = np.loadtxt(join(script_dir,missing_data_dir,'index_mask.txt'),delimiter=' ',dtype=str)
# Using 'brute' method since we only want to do one query per classifier
# so this will be quicker as it avoids overhead of creating a search tree
knn_m = KNeighborsClassifier(algorithm='brute',n_neighbors=k)
prob_Y_hat = np.zeros((raw_im_data.shape[0],int(np.max(trY)+1)))
total_images = raw_im_data.shape[0]
pbar = progressbar.ProgressBar(widgets=[progressbar.FormatLabel('\rProcessed %(value)d of %(max)d Images '), progressbar.Bar()], maxval=total_images, term_width=50).start()
for i in range(total_images):
mask_im=load_image(join(script_dir,missing_data_dir,raw_mask_data[i][0]), input_shape,1).reshape(np.prod(input_shape))
mask = np.logical_not(mask_im > eps) # since mask is 1 at missing locations
v_im=load_image(join(script_dir,missing_data_dir,raw_im_data[i][0]), input_shape, 255).reshape(np.prod(input_shape))
rep_mask = np.tile(mask,(trX.shape[0],1))
# Corrupt whole training set according to the current mask
corr_trX = np.multiply(trX, rep_mask)
knn_m.fit(corr_trX, trY)
prob_Y_hat[i,:] = knn_m.predict_proba(v_im.reshape(1,-1))
pbar.update(i)
pbar.finish()
return prob_Y_hat Example 27
def cochleagram_extractor(xx, sr, win_len, shift_len, channel_number, win_type):
fcoefs, f = make_erb_filters(sr, channel_number, 50)
fcoefs = np.flipud(fcoefs)
xf = erb_frilter_bank(xx, fcoefs)
if win_type == 'hanning':
window = np.hanning(channel_number)
elif win_type == 'hamming':
window = np.hamming(channel_number)
elif win_type == 'triangle':
window = (1 - (np.abs(channel_number - 1 - 2 * np.arange(1, channel_number + 1, 1)) / (channel_number + 1)))
else:
window = np.ones(channel_number)
window = window.reshape((channel_number, 1))
xe = np.power(xf, 2.0)
frames = 1 + ((np.size(xe, 1)-win_len) // shift_len)
cochleagram = np.zeros((channel_number, frames))
for i in range(frames):
one_frame = np.multiply(xe[:, i*shift_len:i*shift_len+win_len], np.repeat(window, win_len, 1))
cochleagram[:, i] = np.sqrt(np.mean(one_frame, 1))
cochleagram = np.where(cochleagram == 0.0, np.finfo(float).eps, cochleagram)
return cochleagram Example 28
def log_power_spectrum_extractor(x, win_len, shift_len, win_type, is_log=False):
samples = x.shape[0]
frames = (samples - win_len) // shift_len
stft = np.zeros((win_len, frames), dtype=np.complex64)
spect = np.zeros((win_len // 2 + 1, frames), dtype=np.float64)
if win_type == 'hanning':
window = np.hanning(win_len)
elif win_type == 'hamming':
window = np.hamming(win_len)
elif win_type == 'rectangle':
window = np.ones(win_len)
for i in range(frames):
one_frame = x[i*shift_len: i*shift_len+win_len]
windowed_frame = np.multiply(one_frame, window)
stft[:, i] = np.fft.fft(windowed_frame, win_len)
if is_log:
spect[:, i] = np.log(np.power(np.abs(stft[0: win_len//2+1, i]), 2.))
else:
spect[:, i] = np.power(np.abs(stft[0: win_len//2+1, i]), 2.)
return spect Example 29
def stft_extractor(x, win_len, shift_len, win_type):
samples = x.shape[0]
frames = (samples - win_len) // shift_len
stft = np.zeros((win_len, frames), dtype=np.complex64)
spect = np.zeros((win_len // 2 + 1, frames), dtype=np.complex64)
if win_type == 'hanning':
window = np.hanning(win_len)
elif win_type == 'hamming':
window = np.hamming(win_len)
elif win_type == 'rectangle':
window = np.ones(win_len)
for i in range(frames):
one_frame = x[i*shift_len: i*shift_len+win_len]
windowed_frame = np.multiply(one_frame, window)
stft[:, i] = np.fft.fft(windowed_frame, win_len)
spect[:, i] = stft[: win_len//2+1, i]
return spect Example 30
def spectrum_extractor(x, win_len, shift_len, win_type, is_log):
samples = x.shape[0]
frames = (samples - win_len) // shift_len
stft = np.zeros((win_len, frames), dtype=np.complex64)
spectrum = np.zeros((win_len // 2 + 1, frames), dtype=np.float64)
if win_type == 'hanning':
window = np.hanning(win_len)
elif win_type == 'hamming':
window = np.hamming(win_len)
elif win_type == 'triangle':
window = (1 - (np.abs(win_len - 1 - 2 * np.arange(1, win_len + 1, 1)) / (win_len + 1)))
else:
window = np.ones(win_len)
for i in range(frames):
one_frame = x[i*shift_len: i*shift_len+win_len]
windowed_frame = np.multiply(one_frame, window)
stft[:, i] = np.fft.fft(windowed_frame, win_len)
if is_log:
spectrum[:, i] = np.log(np.abs(stft[0: win_len//2+1, i]))
else:
spectrum[:, i] = np.abs(stft[0: win_len // 2 + 1:, i])
return spectrum Example 31
def get_volume_of_dose(self, dose, **kwargs):
volumes = np.zeros(self.count)
for x in range(0, self.count):
dvh = np.zeros(len(self.dvh))
for y in range(0, len(self.dvh)):
dvh[y] = self.dvh[y][x]
if 'input' in kwargs and kwargs['input'] == 'relative':
if isinstance(self.rx_dose[x], basestring):
volumes[x] = 0
else:
volumes[x] = volume_of_dose(dvh, dose * self.rx_dose[x])
else:
volumes[x] = volume_of_dose(dvh, dose)
if 'output' in kwargs and kwargs['output'] == 'absolute':
volumes = np.multiply(volumes, self.volume[0:self.count])
else:
volumes = np.multiply(volumes, 100.)
return volumes.tolist() Example 32
def phaseSensitive(self):
"""
Computation of Phase Sensitive Mask. As appears in :
H Erdogan, John R. Hershey, Shinji Watanabe, and Jonathan Le Roux,
"Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks,"
in ICASSP 2015, Brisbane, April, 2015.
Args:
mTarget: (2D ndarray) Magnitude Spectrogram of the target component
pTarget: (2D ndarray) Phase Spectrogram of the output component
mY: (2D ndarray) Magnitude Spectrogram of the output component
pY: (2D ndarray) Phase Spectrogram of the output component
Returns:
mask: (2D ndarray) Array that contains time frequency gain values
"""
print('Phase Sensitive Masking.')
# Compute Phase Difference
Theta = (self._pTarget - self._pY)
self._mask = 2./ (1. + np.exp(-np.multiply(np.divide(self._sTarget, self._eps + self._nResidual), np.cos(Theta)))) - 1. Example 33
def next_frame(self, pixels, t, collaboration_state, osc_data):
# render every 2 frames so the ripples are slower
self.frameCount += 1
if (self.frameCount % 2 == 0):
pixels[:, :] = self.get_pixels()
return
# only generate a ripple every couple frames
if (random.random() < 0.12):
self.start_ripple()
# calculate a pixel values based on it's neighbors
self.ripple_state[1:-1, 1:-1] = (
self.previous_ripple_state[:-2, 1:-1] + self.previous_ripple_state[2:, 1:-1] +
self.previous_ripple_state[1:-1, :-2] + self.previous_ripple_state[1:-1, 2:]
) * 0.5 - self.ripple_state[1:-1, 1:-1]
# damping
# numpy doesn't like multiplying ints and floats so tell it to be unsafe
np.multiply(self.ripple_state, self.damping, out=self.ripple_state, casting='unsafe')
pixels[:, :] = self.get_pixels()
self.swap_buffers() Example 34
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 35
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 36
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 37
def imresizemex(inimg, weights, indices, dim):
in_shape = inimg.shape
w_shape = weights.shape
out_shape = list(in_shape)
out_shape[dim] = w_shape[0]
outimg = np.zeros(out_shape)
if dim == 0:
for i_img in range(in_shape[1]):
for i_w in range(w_shape[0]):
w = weights[i_w, :]
ind = indices[i_w, :]
im_slice = inimg[ind, i_img].astype(np.float64)
outimg[i_w, i_img] = np.sum(np.multiply(np.squeeze(im_slice, axis=0), w.T), axis=0)
elif dim == 1:
for i_img in range(in_shape[0]):
for i_w in range(w_shape[0]):
w = weights[i_w, :]
ind = indices[i_w, :]
im_slice = inimg[i_img, ind].astype(np.float64)
outimg[i_img, i_w] = np.sum(np.multiply(np.squeeze(im_slice, axis=0), w.T), axis=0)
if inimg.dtype == np.uint8:
outimg = np.clip(outimg, 0, 255)
return np.around(outimg).astype(np.uint8)
else:
return outimg Example 38
def test_cputensor_multiply_constant():
"""TODO."""
M = ng.make_axis(length=1)
N = ng.make_axis(length=3)
np_a = np.array([[1, 2, 3]], dtype=np.float32)
np_c = np.multiply(np_a, 2)
a = ng.constant(np_a, [M, N])
b = ng.constant(2)
c = ng.multiply(a, b)
with executor(c) as ex:
result = ex()
print(result)
assert np.array_equal(result, np_c) Example 39
def test_cputensor_fusion():
"""TODO."""
M = ng.make_axis(length=1)
N = ng.make_axis(length=3)
np_a = np.array([[1, 2, 3]], dtype=np.float32)
np_b = np.array([[3, 2, 1]], dtype=np.float32)
np_d = np.multiply(np_b, np.add(np_a, 2))
a = ng.constant(np_a, [M, N])
b = ng.constant(np_b, [M, N])
c = ng.constant(2)
d = ng.multiply(b, ng.add(a, c))
with executor(d) as ex:
result = ex()
print(result)
assert np.array_equal(result, np_d) Example 40
def discrete_uniform(self, low, high, quantum, axes, dtype=None):
"""
Returns a tensor initialized with a discrete uniform distribution.
Arguments:
low: The lower limit of the values.
high: The upper limit of the values.
quantum: Distance between values.
axes: The axes of the tensor.
Returns:
The tensor.
"""
if dtype is None:
dtype = self.dtype
n = math.floor((high - low) / quantum)
result = np.array(self.rng.random_integers(
0, n, ng.make_axes(axes).lengths), dtype=dtype)
np.multiply(result, quantum, result)
np.add(result, low, result)
return result Example 41
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 42
def train(self):
eps = 1e-10
for i in range(self.epo):
if i % 1 == 0:
self.show_error()
A = np.asarray(self.A.copy())
Z = np.asarray(self.Z.copy())
start = time.time()
Z1 = np.multiply(Z, np.asarray(self.A.transpose() * self.Y))
Z = np.divide(Z1, eps + np.asarray(self.A.transpose() * self.A * self.Z)) # + eps to avoid divided by 0
self.Z = np.asmatrix(Z)
A1 = np.multiply(A, np.asarray( self.Y * self.Z.transpose()))
A = np.divide(A1, eps + np.asarray( self.A * self.Z * self.Z.transpose()))
end = time.time()
self.A = np.asmatrix(A)
self.time = self.time + end - start Example 43
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 Example 44
def plot_defect_classifications(bmp, list_of_classified_defects, unclassified_defect_region, td_classify, defect_free_region):
plt.rcParams['figure.figsize'] = (10.0, 10.0);
plt.set_cmap('gray');
fig = plt.figure();
ax = fig.add_subplot(111);
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None);
# Plot the labeled defect regions on top of the temperature field
bmp[defect_free_region==1.] = 0.5*bmp[defect_free_region==1.] # Defect-free region
txt_out = []
for defect in list_of_classified_defects:
defect_center = centroid(defect['defect_region'])
outline = defect['defect_region'] ^ morphology.binary_dilation(defect['defect_region'],morphology.disk(2))
bmp[outline==1] = 255
txt = ax.annotate(DEFECT_TYPES[defect['defect_type']],(defect_center[0]-5,defect_center[1]), color='white', fontweight='bold', fontsize=10);
txt.set_path_effects([PathEffects.withStroke(linewidth=2, foreground='k')]);
txt_out.append(txt)
unknown_td = np.multiply(unclassified_defect_region, (td_classify != 0).astype(np.int))
bmp[morphology.binary_dilation(unknown_td,morphology.disk(2))==1] = 0
bmp[morphology.binary_dilation(unknown_td,morphology.disk(1))==1] = 255
frame = ax.imshow(bmp);
ax.axis('off');
return fig, ax, frame, txt_out Example 45
def mult(self, target, deps, geo_mean_flag, tfo):
#SUPPORT NONE TARGET
target_vec = self.word_vecs.represent(target)
scores = self.word_vecs.pos_scores(target_vec)
for dep in deps:
if dep in self.context_vecs:
dep_vec = self.context_vecs.represent(dep)
mult_scores = self.word_vecs.pos_scores(dep_vec)
if geo_mean_flag:
mult_scores = mult_scores**(1.0/len(deps))
scores = np.multiply(scores, mult_scores)
else:
tfo.write("NOTICE: %s not in context embeddings. Ignoring.\n" % dep)
result_vec = self.word_vecs.top_scores(scores, -1)
return result_vec Example 46
def getTopWeightedFeatures(experiment_id, inst_exp_id, instance_id, size):
instance_id = int(instance_id)
exp = ExperimentFactory.getFactory().fromJson(experiment_id, session)
validation_experiment = ExperimentFactory.getFactory().fromJson(inst_exp_id, session)
#get the features
features_names, features_values = validation_experiment.getFeatures(instance_id)
features_values = [float(value) for value in features_values]
#get the pipeline with scaler and logistic model
pipeline = exp.getModelPipeline()
#scale the features
scaled_values = pipeline.named_steps['scaler'].transform(np.reshape(features_values,(1, -1)))
weighted_values = np.multiply(scaled_values, pipeline.named_steps['model'].coef_)
features = map(lambda name, value, w_value: (name, value, w_value),
features_names, features_values, weighted_values[0])
features.sort(key = lambda tup: abs(tup[2]))
features = features[:-int(size)-1:-1]
tooltips = [x[1] for x in features]
barplot = BarPlot([x[0] for x in features])
dataset = PlotDataset([x[2] for x in features], None)
dataset.setColor(colors_tools.red)
barplot.addDataset(dataset)
return jsonify(barplot.toJson(tooltip_data = tooltips)) Example 47
def __init__(self, images, labels, fake_data=False):
"""Construct a DataSet. """
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape: %s' % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(np.float32)
images = np.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels Example 48
def prop_backward(self, X, y):
layers_rev = list(reversed(self.layers))
Zs_rev = list(reversed(self.Zs))
As_rev = list(reversed(self.As))
As_rev.append(X)
delta0 = np.multiply(-(y - As_rev[0]), self.sigma_prime(Zs_rev[0]))
djdw0 = np.dot(As_rev[1].T, delta0)
self.deltas = [delta0]
self.djdws = [djdw0]
for i in xrange(0, len(layers_rev) - 1):
delta_n = np.dot(self.deltas[i], layers_rev[i].W.T) * \
self.sigma_prime(Zs_rev[i + 1])
djdw_n = np.dot(As_rev[i + 2].T, delta_n)
self.deltas.append(delta_n)
self.djdws.append(djdw_n)
self.deltas = list(reversed(self.deltas))
self.djdws = list(reversed(self.djdws)) Example 49
def prop_backward(self, X, y):
layers_rev = list(reversed(self.layers))
Zs_rev = list(reversed(self.Zs))
As_rev = list(reversed(self.As))
As_rev.append(X)
delta0 = np.multiply(-(y - As_rev[0]), self.sigma_prime(Zs_rev[0]))
djdw0 = np.dot(As_rev[1].T, delta0)
self.deltas = [delta0]
self.djdws = [djdw0]
for i in xrange(0, len(layers_rev) - 1):
delta_n = np.dot(self.deltas[i], layers_rev[i].W.T) * \
self.sigma_prime(Zs_rev[i + 1])
djdw_n = np.dot(As_rev[i + 2].T, delta_n)
self.deltas.append(delta_n)
self.djdws.append(djdw_n)
self.deltas = list(reversed(self.deltas))
self.djdws = list(reversed(self.djdws)) Example 50
def __init__(self, images, labels, fake_data=False):
if fake_data:
self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0 