The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def preprocess(image):
"""Takes an image and apply preprocess"""
# ????????????
image = cv2.resize(image, (data_shape, data_shape))
# ?? BGR ? RGB
image = image[:, :, (2, 1, 0)]
# ?mean?????float
image = image.astype(np.float32)
# ? mean
image -= np.array([123, 117, 104])
# ??? [batch-channel-height-width]
image = np.transpose(image, (2, 0, 1))
image = image[np.newaxis, :]
# ?? ndarray
image = nd.array(image)
return image Example 2
def transform(self, img, lbl):
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= self.mean
img = m.imresize(img, (self.img_size[0], self.img_size[1]))
# Resize scales images from 0 to 255, thus we need
# to divide by 255.0
img = img.astype(float) / 255.0
# NHWC -> NCWH
img = img.transpose(2, 0, 1)
lbl[lbl==255] = 0
lbl = lbl.astype(float)
lbl = m.imresize(lbl, (self.img_size[0], self.img_size[1]), 'nearest', mode='F')
lbl = lbl.astype(int)
img = torch.from_numpy(img).float()
lbl = torch.from_numpy(lbl).long()
return img, lbl Example 3
def backPropagate(Z1, Z2, y, W2, b2):
## YOUR CODE HERE ##
E2 = 0
E1 = 0
Eb1 = 0
# E2 is the error in output layer. To find it we should exract estimated value from actual output.
# We should find 5 error because there are 5 node in output layer.
E2 = Z2 - y
## E1 is the error in the hidden layer. To find it we should use the error that we found in output layer and the weights between
## output and hidden layer
## We should find 30 error because there are 30 node in hidden layer.
E1 = np.dot(W2, np.transpose(E2))
## Eb1 is the error bias for hidden layer. To find it we should use the error that we found in output layer and the weights between
## output and bias layer
## We should find 1 error because there are 1 bias node in hidden layer.
Eb1 = np.dot(b2, np.transpose(E2))
####################
return E2, E1, Eb1
# calculate the gradients for weights between units and the bias weights Example 4
def format_img(img, C): img_min_side = float(C.im_size) (height,width,_) = img.shape if width <= height: f = img_min_side/width new_height = int(f * height) new_width = int(img_min_side) else: f = img_min_side/height new_width = int(f * width) new_height = int(img_min_side) fx = width/float(new_width) fy = height/float(new_height) img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC) img = img[:, :, (2, 1, 0)] img = img.astype(np.float32) img[:, :, 0] -= C.img_channel_mean[0] img[:, :, 1] -= C.img_channel_mean[1] img[:, :, 2] -= C.img_channel_mean[2] img /= C.img_scaling_factor img = np.transpose(img, (2, 0, 1)) img = np.expand_dims(img, axis=0) return img, fx, fy
Example 5
def transform(self, img, lbl):
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= self.mean
img = m.imresize(img, (self.img_size[0], self.img_size[1]))
# Resize scales images from 0 to 255, thus we need
# to divide by 255.0
img = img.astype(float) / 255.0
# NHWC -> NCWH
img = img.transpose(2, 0, 1)
lbl = self.encode_segmap(lbl)
classes = np.unique(lbl)
lbl = lbl.astype(float)
lbl = m.imresize(lbl, (self.img_size[0], self.img_size[1]), 'nearest', mode='F')
lbl = lbl.astype(int)
assert(np.all(classes == np.unique(lbl)))
img = torch.from_numpy(img).float()
lbl = torch.from_numpy(lbl).long()
return img, lbl Example 6
def calcGrads(X, Z1, Z2, E1, E2, Eb1):
## YOUR CODE HERE ##
d_W1 = 0
d_b1 = 0
d_W2 = 0
d_b2 = 0
## In here we should the derivatives for gradients. To find derivative, we should multiply.
# d_w2 is the derivative for weights between hidden layer and the output layer.
d_W2 = np.dot(np.transpose(E2), Z1)
# d_w1 is the derivative for weights between hidden layer and the input layer.
d_W1 = np.dot(E1, X)
# d_b2 is the derivative for weights between hidden layer bias and the output layer.
d_b2 = np.dot(np.transpose(E2), Eb1)
# d_b1 is the derivative for weights between hidden layer bias and the input layer.
d_b1 = np.dot(np.transpose(E1), 1)
####################
return d_W1, d_W2, d_b1, d_b2
# update the weights between units and the bias weights using a learning rate of alpha Example 7
def updateWeights(W1, b1, W2, b2, alpha, d_W1, d_W2, d_b1, d_b2):
## YOUR CODE HERE ##
# W1 = 0
# b1 = 0
# W2 = 0
# b2 = 0
## Here we should update weights with usin the result that we found in calcGrads function
## W1 is weights between input and the hidden layer
W1 = W1 - alpha * (np.transpose(d_W1)) # 400*30
## W2 is weights between output and the hidden layer
W2 = W2 - alpha * (np.transpose(d_W2)) # 30*5
## b1 is weights between input bias and the hidden layer
b1 = b1 - alpha * d_b1
## b2 is weights between hidden layer bias and the output layer
b2 = b2 - alpha * (np.transpose(d_b2))
####################
return W1, b1, W2, b2 Example 8
def make_heatmaps_from_joints(input_size, heatmap_size, gaussian_variance, batch_joints):
# Generate ground-truth heatmaps from ground-truth 2d joints
scale_factor = input_size // heatmap_size
batch_gt_heatmap_np = []
for i in range(batch_joints.shape[0]):
gt_heatmap_np = []
invert_heatmap_np = np.ones(shape=(heatmap_size, heatmap_size))
for j in range(batch_joints.shape[1]):
cur_joint_heatmap = make_gaussian(heatmap_size,
gaussian_variance,
center=(batch_joints[i][j] // scale_factor))
gt_heatmap_np.append(cur_joint_heatmap)
invert_heatmap_np -= cur_joint_heatmap
gt_heatmap_np.append(invert_heatmap_np)
batch_gt_heatmap_np.append(gt_heatmap_np)
batch_gt_heatmap_np = np.asarray(batch_gt_heatmap_np)
batch_gt_heatmap_np = np.transpose(batch_gt_heatmap_np, (0, 2, 3, 1))
return batch_gt_heatmap_np Example 9
def af_h5_to_np(input_path, outpath):
files = tables.open_file(input_path, mode = 'r+')
speaker_nodes = files.root._f_list_nodes()
for spk in speaker_nodes:
file_nodes = spk._f_list_nodes()
for fls in file_nodes:
file_name = fls._v_name
af_nodes = fls._f_list_nodes()
af_list = []
for fts in af_nodes:
features = fts[:]
mean = numpy.mean(features,1)
normalised_feats = list(numpy.transpose(features)/mean)
af_list += normalised_feats
numpy.save(outpath + file_name, numpy.array(af_list)) Example 10
def mahalanobis_distance(difference, num_random_features):
num_samples, _ = np.shape(difference)
sigma = np.cov(np.transpose(difference))
mu = np.mean(difference, 0)
if num_random_features == 1:
stat = float(num_samples * mu ** 2) / float(sigma)
else:
try:
linalg.inv(sigma)
except LinAlgError:
print('covariance matrix is singular. Pvalue returned is 1.1')
warnings.warn('covariance matrix is singular. Pvalue returned is 1.1')
return 0
stat = num_samples * mu.dot(linalg.solve(sigma, np.transpose(mu)))
return chi2.sf(stat, num_random_features) Example 11
def sumIntensitiesMeme(
self,
t,
m,
node_vec,
etimes,
filterlatertimes=True,
):
if filterlatertimes:
I = self.mu * self.gamma[m] \
+ np.dot(np.transpose(self.alpha[node_vec[etimes
< t].astype(int), :][:, range(self.D)]),
self.kernel_evaluate(t, etimes[etimes < t],
self.omega))
else:
I = self.mu * self.gamma[m] \
+ np.dot(np.transpose(self.alpha[node_vec.astype(int), :
][:, range(self.D)]), self.kernel_evaluate(t,
etimes, self.omega))
sumI = np.sum(I)
return (I, sumI) Example 12
def sumIntensitiesAll(
self,
t,
node_vec,
etimes,
filterlatertimes=False,
):
if filterlatertimes:
I = self.mu * np.sum(self.gamma) \
+ np.dot(np.transpose(self.alpha[node_vec[etimes
< t].astype(int), :][:, range(self.D)]),
self.kernel_evaluate(t, etimes[etimes < t],
self.omega))
else:
I = self.mu * np.sum(self.gamma) \
+ np.dot(np.transpose(self.alpha[node_vec.astype(int), :
][:, range(self.D)]), self.kernel_evaluate(t,
etimes, self.omega))
sumI = np.sum(I)
return (I, sumI) Example 13
def _intensityUserMeme(
self,
t,
d,
m,
filterlatertimes=False,
):
etimes = self.etimes[self.eventmemes == m]
node_vec = self.node_vec[self.eventmemes == m]
if filterlatertimes:
return self.mu[d] * self.gamma[m] \
+ np.dot(np.transpose(self.alpha[node_vec[etimes
< t].astype(int), :][:, d]),
self.kernel_evaluate(t, etimes[etimes < t],
self.omega))
else:
return self.mu[d] * self.gamma[m] \
+ np.dot(np.transpose(self.alpha[node_vec.astype(int), :
][:, d]), self.kernel_evaluate(t, etimes,
self.omega)) Example 14
def load_X(X_signals_paths):
"""
Given attribute (train or test) of feature, read all 9 features into an
np ndarray of shape [sample_sequence_idx, time_step, feature_num]
argument: X_signals_paths str attribute of feature: 'train' or 'test'
return: np ndarray, tensor of features
"""
X_signals = []
for signal_type_path in X_signals_paths:
file = open(signal_type_path, 'rb')
# Read dataset from disk, dealing with text files' syntax
X_signals.append(
[np.array(serie, dtype=np.float32) for serie in [
row.replace(' ', ' ').strip().split(' ') for row in file
]]
)
file.close()
return np.transpose(np.array(X_signals), (1, 2, 0)) Example 15
def get_batcher(self, shuffle=True, augment=True):
""" produces batch generator """
w, h = self.resize
if shuffle: np.random.shuffle(self.data)
data = iter(self.data)
while True:
x = np.zeros((self.batch_size, self.timesteps, h, w, 3))
y = np.zeros((self.batch_size, 1))
for b in range(self.batch_size):
images, label = next(data)
for t, img_name in enumerate(images):
image_path = self.folder + 'images/' + img_name
img = cv2.imread(image_path)
img = img[190:350, 100:520] # crop
if augment:
img = aug.augment_image(img) # augmentation
img = cv2.resize(img.copy(), (w, h))
x[b, t] = img
y[b] = label
x = np.transpose(x, [0, 4, 1, 2, 3])
yield x, y Example 16
def _random_op(sites, ldim, hermitian=False, normalized=False, randstate=None,
dtype=np.complex_):
"""Returns a random operator of shape (ldim,ldim) * sites with local
dimension `ldim` living on `sites` sites in global form.
:param sites: Number of local sites
:param ldim: Local ldimension
:param hermitian: Return only the hermitian part (default False)
:param normalized: Normalize to Frobenius norm=1 (default False)
:param randstate: numpy.random.RandomState instance or None
:returns: numpy.ndarray of shape (ldim,ldim) * sites
>>> A = _random_op(3, 2); A.shape
(2, 2, 2, 2, 2, 2)
"""
op = _randfuncs[dtype]((ldim**sites,) * 2, randstate=randstate)
if hermitian:
op += np.transpose(op).conj()
if normalized:
op /= np.linalg.norm(op)
return op.reshape((ldim,) * 2 * sites) Example 17
def transpose(self, axes=None):
"""Transpose (=reverse order of) physical legs on each site
:param axes: New order of the physical axes. If ``None`` is passed,
we reverse the order of the legs on each site. (default ``None``)
>>> from .factory import random_mpa
>>> mpa = random_mpa(2, (2, 3, 4), 2)
>>> mpa.shape
((2, 3, 4), (2, 3, 4))
>>> mpa.transpose((2, 0, 1)).shape
((4, 2, 3), (4, 2, 3))
"""
ltens = LocalTensors((_local_transpose(tens, axes) for tens in self.lt),
cform=self.canonical_form)
return type(self)(ltens) Example 18
def cal_hist(self, t1, t2, data1_maxlen, hist_size):
mhist = np.zeros((data1_maxlen, hist_size), dtype=np.float32)
d1len = len(self.data1[t1])
if self.use_hist_feats:
assert (t1, t2) in self.hist_feats
caled_hist = np.reshape(self.hist_feats[(t1, t2)], (d1len, hist_size))
if d1len < data1_maxlen:
mhist[:d1len, :] = caled_hist[:, :]
else:
mhist[:, :] = caled_hist[:data1_maxlen, :]
else:
t1_rep = self.embed[self.data1[t1]]
t2_rep = self.embed[self.data2[t2]]
mm = t1_rep.dot(np.transpose(t2_rep))
for (i,j), v in np.ndenumerate(mm):
if i >= data1_maxlen:
break
vid = int((v + 1.) / 2. * ( hist_size - 1.))
mhist[i][vid] += 1.
mhist += 1.
mhist = np.log10(mhist)
return mhist Example 19
def cal_hist(self, t1, t2, data1_maxlen, hist_size):
mhist = np.zeros((data1_maxlen, hist_size), dtype=np.float32)
t1_cont = list(self.data1[t1])
t2_cont = list(self.data2[t2])
d1len = len(t1_cont)
if self.use_hist_feats:
assert (t1, t2) in self.hist_feats
caled_hist = np.reshape(self.hist_feats[(t1, t2)], (d1len, hist_size))
if d1len < data1_maxlen:
mhist[:d1len, :] = caled_hist[:, :]
else:
mhist[:, :] = caled_hist[:data1_maxlen, :]
else:
t1_rep = self.embed[t1_cont]
t2_rep = self.embed[t2_cont]
mm = t1_rep.dot(np.transpose(t2_rep))
for (i,j), v in np.ndenumerate(mm):
if i >= data1_maxlen:
break
vid = int((v + 1.) / 2. * ( hist_size - 1.))
mhist[i][vid] += 1.
mhist += 1.
mhist = np.log10(mhist)
return mhist Example 20
def cal_hist(self, t1, t2, data1_maxlen, hist_size):
mhist = np.zeros((data1_maxlen, hist_size), dtype=np.float32)
t1_cont = list(self.data1[t1])
t2_cont = list(self.data2[t2])
d1len = len(t1_cont)
if self.use_hist_feats:
assert (t1, t2) in self.hist_feats
curr_pair_feats = list(self.hist_feats[(t1, t2)])
caled_hist = np.reshape(curr_pair_feats, (d1len, hist_size))
if d1len < data1_maxlen:
mhist[:d1len, :] = caled_hist[:, :]
else:
mhist[:, :] = caled_hist[:data1_maxlen, :]
else:
t1_rep = self.embed[t1_cont]
t2_rep = self.embed[t2_cont]
mm = t1_rep.dot(np.transpose(t2_rep))
for (i,j), v in np.ndenumerate(mm):
if i >= data1_maxlen:
break
vid = int((v + 1.) / 2. * ( hist_size - 1.))
mhist[i][vid] += 1.
mhist += 1.
mhist = np.log10(mhist)
return mhist Example 21
def cal_hist(self, t1, t2, data1_maxlen, hist_size):
mhist = np.zeros((data1_maxlen, hist_size), dtype=np.float32)
t1_cont = list(self.data1[t1])
t2_cont = list(self.data2[t2])
d1len = len(t1_cont)
if self.use_hist_feats:
assert (t1, t2) in self.hist_feats
caled_hist = np.reshape(self.hist_feats[(t1, t2)], (d1len, hist_size))
if d1len < data1_maxlen:
mhist[:d1len, :] = caled_hist[:, :]
else:
mhist[:, :] = caled_hist[:data1_maxlen, :]
else:
t1_rep = self.embed[t1_cont]
t2_rep = self.embed[t2_cont]
mm = t1_rep.dot(np.transpose(t2_rep))
for (i,j), v in np.ndenumerate(mm):
if i >= data1_maxlen:
break
vid = int((v + 1.) / 2. * ( hist_size - 1.))
mhist[i][vid] += 1.
mhist += 1.
mhist = np.log10(mhist)
return mhist Example 22
def cal_hist(self, t1, t2, data1_maxlen, hist_size):
mhist = np.zeros((data1_maxlen, hist_size), dtype=np.float32)
t1_cont = list(self.data1[t1])
t2_cont = list(self.data2[t2])
d1len = len(t1_cont)
if self.use_hist_feats:
assert (t1, t2) in self.hist_feats
curr_pair_feats = list(self.hist_feats[(t1, t2)])
caled_hist = np.reshape(curr_pair_feats, (d1len, hist_size))
if d1len < data1_maxlen:
mhist[:d1len, :] = caled_hist[:, :]
else:
mhist[:, :] = caled_hist[:data1_maxlen, :]
else:
t1_rep = self.embed[t1_cont]
t2_rep = self.embed[t2_cont]
mm = t1_rep.dot(np.transpose(t2_rep))
for (i,j), v in np.ndenumerate(mm):
if i >= data1_maxlen:
break
vid = int((v + 1.) / 2. * ( hist_size - 1.))
mhist[i][vid] += 1.
mhist += 1.
mhist = np.log10(mhist)
return mhist Example 23
def calc_score_of_histories(words, dropout=0.0):
# This will change from a list of histories, to a list of words in each history position
words = np.transpose(words)
# Lookup the embeddings and concatenate them
emb = dy.concatenate([dy.lookup_batch(W_emb, x) for x in words])
# Create the hidden layer
W_h = dy.parameter(W_h_p)
b_h = dy.parameter(b_h_p)
h = dy.tanh(dy.affine_transform([b_h, W_h, emb]))
# Perform dropout
if dropout != 0.0:
h = dy.dropout(h, dropout)
# Calculate the score and return
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
return dy.affine_transform([b_sm, W_sm, h])
# Calculate the loss value for the entire sentence Example 24
def reSample( df , dt = None , xAxis = None , n = None , kind = 'linear') :
""" re-sample the signal """
if type(df) == pd.Series : df = pd.DataFrame(df)
f = interp1d( df.index, np.transpose(df.values) , kind=kind, axis=-1, copy=True, bounds_error=True, assume_sorted=True)
if dt :
end = int(+(df.index[-1] - df.index[0] ) / dt) * dt + df.index[0]
xAxis = np.linspace( df.index[0] , end , 1+int(+(end - df.index[0] ) / dt) )
elif n :
xAxis = np.linspace( df.index[0] , df.index[-1] , n )
elif xAxis == None :
raise(Exception("reSample : either dt or xAxis should be provided" ))
#For rounding issue, ensure that xAxis is within ts.xAxis
#xAxis[ np.where( xAxis > np.max(df.index[:]) ) ] = df.index[ np.where( xAxis > np.max(df.index[:]) ) ]
return pd.DataFrame( data = np.transpose(f(xAxis)), index = xAxis , columns = map( lambda x : "reSample("+ x +")" , df.columns ) ) Example 25
def getPSD( df , dw = 0.05, roverlap = 0.5, window='hanning', detrend='constant') :
"""
Compute the power spectral density
"""
if type(df) == pd.Series : df = pd.DataFrame(df)
nfft = int ( (2*pi / dw) / dx(df) )
nperseg = 2**int(log(nfft)/log(2))
noverlap = nperseg * roverlap
""" Return the PSD of a time signal """
try :
from scipy.signal import welch
except :
raise Exception("Welch function not found, please install scipy > 0.12")
data = []
for iSig in range(df.shape[1]) :
test = welch( df.values[:,iSig] , fs = 1. / dx(df) , window=window, nperseg=nperseg, noverlap=noverlap, nfft=nfft, detrend=detrend, return_onesided=True, scaling='density')
data.append( test[1] / (2*pi) )
xAxis = test[0][:] * 2*pi
return pd.DataFrame( data = np.transpose(data), index = xAxis , columns = [ "psd("+ str(x) +")" for x in df.columns ] ) Example 26
def derivFFT(df, n=1 ) :
""" Deriv a signal trought FFT, warning, edge can be a bit noisy...
indexList : channel to derive
n : order of derivation
"""
deriv = []
for iSig in range(df.shape[1]) :
fft = np.fft.fft( df.values[:,iSig] ) #FFT
freq = np.fft.fftfreq( df.shape[0] , dx(df) )
from copy import deepcopy
fft0 = deepcopy(fft)
if n>0 :
fft *= (1j * 2*pi* freq[:])**n #Derivation in frequency domain
else :
fft[-n:] *= (1j * 2*pi* freq[-n:])**n
fft[0:-n] = 0.
tts = np.real(np.fft.ifft(fft))
tts -= tts[0]
deriv.append( tts ) #Inverse FFT
return pd.DataFrame( data = np.transpose(deriv), index = df.index , columns = [ "DerivFFT("+ x +")" for x in df.columns ] ) Example 27
def _starts_with_output(data, col):
'''
Helper function for to_integers in cases where
the feature is categorized based on a common
first character of a string.
'''
data[col] = data[col].fillna('0')
temp_df = _category_starts_with(data, col)
temp_df['start_char'] = temp_df[0]
temp_df = temp_df.drop(0, axis=1)
reference_df = temp_df.set_index('start_char').transpose()
temp_list = []
for i in range(len(data[col])):
for c in temp_df['start_char']:
if data[col][i].startswith(c) == True:
temp_list.append(reference_df[c][0])
if len(data[col]) != len(temp_list):
print "AUTONOMIO ERROR: length of input and output do not match"
else:
return pd.Series(temp_list) Example 28
def reduce_height(img4, eng):
"""
Reduces the height by 1 pixel
Args:
img4 (n,m,4 numpy matrix): RGB image with additional mask layer.
eng (n,m numpy matrix): Pre-computed energy matrix for supplied image.
Returns:
tuple (
n,1 numpy matrix: the removed seam,
n-1,m,4 numpy matrix: The height-redcued image,
float: The cost of the seam removed
)
"""
flipped_eng = np.transpose(eng)
flipped_img4 = np.transpose(img4, (1, 0, 2))
flipped_seam, reduced_flipped_img4, cost = reduce_width(flipped_img4, flipped_eng)
return (
np.transpose(flipped_seam),
np.transpose(reduced_flipped_img4, (1, 0, 2)),
cost
) Example 29
def format_img(img, C): img_min_side = float(C.im_size) (height,width,_) = img.shape if width <= height: f = img_min_side/width new_height = int(f * height) new_width = int(img_min_side) else: f = img_min_side/height new_width = int(f * width) new_height = int(img_min_side) fx = width/float(new_width) fy = height/float(new_height) img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC) img = img[:, :, (2, 1, 0)] img = img.astype(np.float32) img[:, :, 0] -= C.img_channel_mean[0] img[:, :, 1] -= C.img_channel_mean[1] img[:, :, 2] -= C.img_channel_mean[2] img /= C.img_scaling_factor img = np.transpose(img, (2, 0, 1)) img = np.expand_dims(img, axis=0) return img, fx, fy
Example 30
def _mutual_reach_dist_MST(dist_tree):
"""
Computes minimum spanning tree of the mutual reach distance complete graph
Args:
dist_tree (np.ndarray): array of dimensions (n_samples, n_samples)
Graph of all pair-wise mutual reachability distances
between points.
Returns: minimum_spanning_tree (np.ndarray)
array of dimensions (n_samples, n_samples)
minimum spanning tree of all pair-wise mutual reachability
distances between points.
"""
mst = minimum_spanning_tree(dist_tree).toarray()
return mst + np.transpose(mst) Example 31
def writeModelUBC(mesh, fileName, model):
"""Writes a model associated with a TensorMesh
to a UBC-GIF format model file.
:param string fileName: File to write to
:param numpy.ndarray model: The model
"""
# Reshape model to a matrix
modelMat = mesh.r(model, 'CC', 'CC', 'M')
# Transpose the axes
modelMatT = modelMat.transpose((2, 0, 1))
# Flip z to positive down
modelMatTR = utils.mkvc(modelMatT[::-1, :, :])
np.savetxt(fileName, modelMatTR.ravel()) Example 32
def test_inner_product_with_various_contiguities(self):
# github issue 6532
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
# check an inner product involving a matrix transpose
A = np.array([[1, 2], [3, 4]], dtype=dt)
B = np.array([[1, 3], [2, 4]], dtype=dt)
C = np.array([1, 1], dtype=dt)
desired = np.array([4, 6], dtype=dt)
assert_equal(np.inner(A.T, C), desired)
assert_equal(np.inner(C, A.T), desired)
assert_equal(np.inner(B, C), desired)
assert_equal(np.inner(C, B), desired)
# check a matrix product
desired = np.array([[7, 10], [15, 22]], dtype=dt)
assert_equal(np.inner(A, B), desired)
# check the syrk vs. gemm paths
desired = np.array([[5, 11], [11, 25]], dtype=dt)
assert_equal(np.inner(A, A), desired)
assert_equal(np.inner(A, A.copy()), desired)
# check an inner product involving an aliased and reversed view
a = np.arange(5).astype(dt)
b = a[::-1]
desired = np.array(10, dtype=dt).item()
assert_equal(np.inner(b, a), desired) Example 33
def test_TakeTransposeInnerOuter(self):
# Test of take, transpose, inner, outer products
x = arange(24)
y = np.arange(24)
x[5:6] = masked
x = x.reshape(2, 3, 4)
y = y.reshape(2, 3, 4)
assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
assert_equal(np.inner(filled(x, 0), filled(y, 0)),
inner(x, y))
assert_equal(np.outer(filled(x, 0), filled(y, 0)),
outer(x, y))
y = array(['abc', 1, 'def', 2, 3], object)
y[2] = masked
t = take(y, [0, 3, 4])
assert_(t[0] == 'abc')
assert_(t[1] == 2)
assert_(t[2] == 3) Example 34
def test_generic_methods(self):
# Tests some MaskedArray methods.
a = array([1, 3, 2])
assert_equal(a.any(), a._data.any())
assert_equal(a.all(), a._data.all())
assert_equal(a.argmax(), a._data.argmax())
assert_equal(a.argmin(), a._data.argmin())
assert_equal(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))
assert_equal(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))
assert_equal(a.conj(), a._data.conj())
assert_equal(a.conjugate(), a._data.conjugate())
m = array([[1, 2], [3, 4]])
assert_equal(m.diagonal(), m._data.diagonal())
assert_equal(a.sum(), a._data.sum())
assert_equal(a.take([1, 2]), a._data.take([1, 2]))
assert_equal(m.transpose(), m._data.transpose()) Example 35
def test_testTakeTransposeInnerOuter(self):
# Test of take, transpose, inner, outer products
x = arange(24)
y = np.arange(24)
x[5:6] = masked
x = x.reshape(2, 3, 4)
y = y.reshape(2, 3, 4)
assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
inner(x, y)))
assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
outer(x, y)))
y = array(['abc', 1, 'def', 2, 3], object)
y[2] = masked
t = take(y, [0, 3, 4])
assert_(t[0] == 'abc')
assert_(t[1] == 2)
assert_(t[2] == 3) Example 36
def test_testArrayMethods(self):
a = array([1, 3, 2])
self.assertTrue(eq(a.any(), a._data.any()))
self.assertTrue(eq(a.all(), a._data.all()))
self.assertTrue(eq(a.argmax(), a._data.argmax()))
self.assertTrue(eq(a.argmin(), a._data.argmin()))
self.assertTrue(eq(a.choose(0, 1, 2, 3, 4),
a._data.choose(0, 1, 2, 3, 4)))
self.assertTrue(eq(a.compress([1, 0, 1]), a._data.compress([1, 0, 1])))
self.assertTrue(eq(a.conj(), a._data.conj()))
self.assertTrue(eq(a.conjugate(), a._data.conjugate()))
m = array([[1, 2], [3, 4]])
self.assertTrue(eq(m.diagonal(), m._data.diagonal()))
self.assertTrue(eq(a.sum(), a._data.sum()))
self.assertTrue(eq(a.take([1, 2]), a._data.take([1, 2])))
self.assertTrue(eq(m.transpose(), m._data.transpose())) Example 37
def test_4(self):
"""
Test of take, transpose, inner, outer products.
"""
x = self.arange(24)
y = np.arange(24)
x[5:6] = self.masked
x = x.reshape(2, 3, 4)
y = y.reshape(2, 3, 4)
assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
self.inner(x, y))
assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
self.outer(x, y))
y = self.array(['abc', 1, 'def', 2, 3], object)
y[2] = self.masked
t = self.take(y, [0, 3, 4])
assert t[0] == 'abc'
assert t[1] == 2
assert t[2] == 3 Example 38
def test_basic(self):
import numpy.linalg as linalg
A = np.array([[1., 2.],
[3., 4.]])
mA = matrix(A)
assert_(np.allclose(linalg.inv(A), mA.I))
assert_(np.all(np.array(np.transpose(A) == mA.T)))
assert_(np.all(np.array(np.transpose(A) == mA.H)))
assert_(np.all(A == mA.A))
B = A + 2j*A
mB = matrix(B)
assert_(np.allclose(linalg.inv(B), mB.I))
assert_(np.all(np.array(np.transpose(B) == mB.T)))
assert_(np.all(np.array(np.transpose(B).conj() == mB.H))) Example 39
def preprocess_vgg19_mil(Image):
if len(Image.shape) == 2:
Image = Image[:, :, np.newaxis]
Image = np.concatenate((Image, Image, Image), axis=2)
mean = np.array([[[103.939, 116.779, 123.68]]]);
base_image_size = 565;
Image = cv2.resize(np.transpose(Image, axes=(1, 2, 0)), (base_image_size, base_image_size), interpolation=cv2.INTER_CUBIC)
Image_orig = Image.astype(np.float32, copy=True)
Image_orig -= mean
im = Image_orig
#im, gr, grr = upsample_image(Image_orig, base_image_size)
# im = cv2.resize(Image_orig, (base_image_size, base_image_size), interpolation=cv2.INTER_CUBIC)
im = np.transpose(im, axes=(2, 0, 1))
im = im[np.newaxis, :, :, :]
return im Example 40
def test_img(im, net, base_image_size, means):
"""
Calls Caffe to get output for this image
"""
batch_size = 1
# Resize image
im_orig = im.astype(np.float32, copy=True)
im_orig -= means
im, gr, grr = upsample_image(im_orig, base_image_size)
im = np.transpose(im, axes=(2, 0, 1))
im = im[np.newaxis, :, :, :]
# Pass into model
mil_prob = net(Variable(torch.from_numpy(im), requires_grad=False).cuda())
return mil_prob Example 41
def build_2D_cov_matrix(sigmax,sigmay,angle,verbose=True):
"""
Build a covariance matrix for a 2D multivariate Gaussian
--- INPUT ---
sigmax Standard deviation of the x-compoent of the multivariate Gaussian
sigmay Standard deviation of the y-compoent of the multivariate Gaussian
angle Angle to rotate matrix by in degrees (clockwise) to populate covariance cross terms
verbose Toggle verbosity
--- EXAMPLE OF USE ---
import tdose_utilities as tu
covmatrix = tu.build_2D_cov_matrix(3,1,35)
"""
if verbose: print ' - Build 2D covariance matrix with varinaces (x,y)=('+str(sigmax)+','+str(sigmay)+\
') and then rotated '+str(angle)+' degrees'
cov_orig = np.zeros([2,2])
cov_orig[0,0] = sigmay**2.0
cov_orig[1,1] = sigmax**2.0
angle_rad = (180.0-angle) * np.pi/180.0 # The (90-angle) makes sure the same convention as DS9 is used
c, s = np.cos(angle_rad), np.sin(angle_rad)
rotmatrix = np.matrix([[c, -s], [s, c]])
cov_rot = np.dot(np.dot(rotmatrix,cov_orig),np.transpose(rotmatrix)) # performing rot * cov * rot^T
return cov_rot
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = Example 42
def get_face_mask(img, img_l):
img = np.zeros(img.shape[:2], dtype = np.float64)
for idx in OVERLAY_POINTS_IDX:
cv2.fillConvexPoly(img, cv2.convexHull(img_l[idx]), color = 1)
img = np.array([img, img, img]).transpose((1, 2, 0))
img = (cv2.GaussianBlur(img, (BLUR_AMOUNT, BLUR_AMOUNT), 0) > 0) * 1.0
img = cv2.GaussianBlur(img, (BLUR_AMOUNT, BLUR_AMOUNT), 0)
return img Example 43
def get_tm_opp(pts1, pts2):
# Transformation matrix - ( Translation + Scaling + Rotation )
# using Procuster analysis
pts1 = np.float64(pts1)
pts2 = np.float64(pts2)
m1 = np.mean(pts1, axis = 0)
m2 = np.mean(pts2, axis = 0)
# Removing translation
pts1 -= m1
pts2 -= m2
std1 = np.std(pts1)
std2 = np.std(pts2)
std_r = std2/std1
# Removing scaling
pts1 /= std1
pts2 /= std2
U, S, V = np.linalg.svd(np.transpose(pts1) * pts2)
# Finding the rotation matrix
R = np.transpose(U * V)
return np.vstack([np.hstack((std_r * R,
np.transpose(m2) - std_r * R * np.transpose(m1))), np.matrix([0.0, 0.0, 1.0])]) Example 44
def show_heatmap(x, y, attention):
#print attention[:len(y),:len(x)]
#print attention[:len(y),:len(x)].shape
#data = np.transpose(attention[:len(y),:len(x)])
data = attention[:len(y),:len(x)]
x, y = y, x
#ax = plt.axes(aspect=0.4)
ax = plt.axes()
heatmap = plt.pcolor(data, cmap=plt.cm.Blues)
xticks = np.arange(len(y)) + 0.5
xlabels = y
yticks = np.arange(len(x)) + 0.5
ylabels = x
plt.xticks(xticks, xlabels, rotation='vertical')
ax.set_yticks(yticks)
ax.set_yticklabels(ylabels)
# make it look less like a scatter plot and more like a colored table
ax.tick_params(axis='both', length=0)
ax.invert_yaxis()
ax.xaxis.tick_top()
plt.colorbar(heatmap)
plt.show()
#plt.savefig('./attention-out.pdf') Example 45
def alterneigh(self, alpha, rad, i, b, g, r):
if i-rad >= self.SPECIALS-1:
lo = i-rad
start = 0
else:
lo = self.SPECIALS-1
start = (self.SPECIALS-1 - (i-rad))
if i+rad <= self.NETSIZE:
hi = i+rad
end = rad*2-1
else:
hi = self.NETSIZE
end = (self.NETSIZE - (i+rad))
a = self.geta(alpha, rad)[start:end]
p = self.network[lo+1:hi]
p -= np.transpose(np.transpose(p - np.array([b, g, r])) * a)
#def contest(self, b, g, r):
# """ Search for biased BGR values
# Finds closest neuron (min dist) and updates self.freq
# finds best neuron (min dist-self.bias) and returns position
# for frequently chosen neurons, self.freq[i] is high and self.bias[i] is negative
# self.bias[i] = self.GAMMA*((1/self.NETSIZE)-self.freq[i])"""
#
# i, j = self.SPECIALS, self.NETSIZE
# dists = abs(self.network[i:j] - np.array([b,g,r])).sum(1)
# bestpos = i + np.argmin(dists)
# biasdists = dists - self.bias[i:j]
# bestbiaspos = i + np.argmin(biasdists)
# self.freq[i:j] -= self.BETA * self.freq[i:j]
# self.bias[i:j] += self.BETAGAMMA * self.freq[i:j]
# self.freq[bestpos] += self.BETA
# self.bias[bestpos] -= self.BETAGAMMA
# return bestbiaspos Example 46
def transform(self, img, lbl):
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= self.mean
img = img.astype(float) / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).float()
lbl = torch.from_numpy(lbl).long()
return img, lbl Example 47
def transform(self, img, lbl):
"""transform
:param img:
:param lbl:
"""
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= self.mean
img = m.imresize(img, (self.img_size[0], self.img_size[1]))
# Resize scales images from 0 to 255, thus we need
# to divide by 255.0
img = img.astype(float) / 255.0
# NHWC -> NCWH
img = img.transpose(2, 0, 1)
classes = np.unique(lbl)
lbl = lbl.astype(float)
lbl = m.imresize(lbl, (self.img_size[0], self.img_size[1]), 'nearest', mode='F')
lbl = lbl.astype(int)
if not np.all(classes == np.unique(lbl)):
print("WARN: resizing labels yielded fewer classes")
if not np.all(np.unique(lbl) < self.n_classes):
raise ValueError("Segmentation map contained invalid class values")
img = torch.from_numpy(img).float()
lbl = torch.from_numpy(lbl).long()
return img, lbl Example 48
def set_anchors(mc):
H, W, B = 13, 18, 9
anchor_shapes = np.reshape(
[np.array(
[[ 36., 37.], [ 366., 174.], [ 115., 59.],
[ 162., 87.], [ 38., 90.], [ 258., 173.],
[ 224., 108.], [ 78., 170.], [ 72., 43.]])] * H * W,
(H, W, B, 2)
)
center_x = np.reshape(
np.transpose(
np.reshape(
np.array([np.arange(1, W+1)*float(mc.IMAGE_WIDTH)/(W+1)]*H*B),
(B, H, W)
),
(1, 2, 0)
),
(H, W, B, 1)
)
center_y = np.reshape(
np.transpose(
np.reshape(
np.array([np.arange(1, H+1)*float(mc.IMAGE_HEIGHT)/(H+1)]*W*B),
(B, W, H)
),
(2, 1, 0)
),
(H, W, B, 1)
)
anchors = np.reshape(
np.concatenate((center_x, center_y, anchor_shapes), axis=3),
(-1, 4)
)
return anchors Example 49
def set_anchors(mc):
H, W, B = 14, 19, 9
anchor_shapes = np.reshape(
[np.array(
[[ 36., 37.], [ 366., 174.], [ 115., 59.],
[ 162., 87.], [ 38., 90.], [ 258., 173.],
[ 224., 108.], [ 78., 170.], [ 72., 43.]])] * H * W,
(H, W, B, 2)
)
center_x = np.reshape(
np.transpose(
np.reshape(
np.array([np.arange(1, W+1)*float(mc.IMAGE_WIDTH)/(W+1)]*H*B),
(B, H, W)
),
(1, 2, 0)
),
(H, W, B, 1)
)
center_y = np.reshape(
np.transpose(
np.reshape(
np.array([np.arange(1, H+1)*float(mc.IMAGE_HEIGHT)/(H+1)]*W*B),
(B, W, H)
),
(2, 1, 0)
),
(H, W, B, 1)
)
anchors = np.reshape(
np.concatenate((center_x, center_y, anchor_shapes), axis=3),
(-1, 4)
)
return anchors Example 50
def convert_to_display(samples):
cnt, height, width = int(math.floor(math.sqrt(samples.shape[0]))), samples.shape[1], samples.shape[2]
samples = np.transpose(samples, axes=[1, 0, 2, 3])
samples = np.reshape(samples, [height, cnt, cnt, width])
samples = np.transpose(samples, axes=[1, 0, 2, 3])
samples = np.reshape(samples, [height*cnt, width*cnt])
return samples 