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 draw_flow(img, flow, step=16):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
fx, fy = flow[y,x].T
m = np.bitwise_and(np.isfinite(fx), np.isfinite(fy))
lines = np.vstack([x[m], y[m], x[m]+fx[m], y[m]+fy[m]]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.polylines(vis, lines, 0, (0, 255, 0))
for (x1, y1), (x2, y2) in lines:
cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
return vis Example 2
def visualize(self, vis, colored=True):
try:
tids = set(self.ids)
except:
return vis
for hid, hbox in izip(self.ids, self.bboxes):
cv2.rectangle(vis, (hbox[0], hbox[1]), (hbox[2], hbox[3]), (0,255,0), 1)
vis = super(BoundingBoxKLT, self).viz(vis, colored=colored)
# for tid, pts in self.tm_.tracks.iteritems():
# if tid not in tids: continue
# cv2.polylines(vis, [np.vstack(pts.items).astype(np.int32)[-4:]], False,
# (0,255,0), thickness=1)
# tl, br = np.int32(pts.latest_item)-2, np.int32(pts.latest_item)+2
# cv2.rectangle(vis, (tl[0], tl[1]), (br[0], br[1]), (0,255,0), -1)
# OpenCVKLT.draw_tracks(self, vis, colored=colored, max_track_length=10)
return vis Example 3
def compute_nystrom(ds_name, use_node_labels, embedding_dim, community_detection_method, kernels):
if ds_name=="SYNTHETIC":
graphs, labels = generate_synthetic()
else:
graphs, labels = load_data(ds_name, use_node_labels)
communities, subgraphs = compute_communities(graphs, use_node_labels, community_detection_method)
print("Number of communities: ", len(communities))
lens = []
for community in communities:
lens.append(community.number_of_nodes())
print("Average size: %.2f" % np.mean(lens))
Q=[]
for idx, k in enumerate(kernels):
model = Nystrom(k, n_components=embedding_dim)
model.fit(communities)
Q_t = model.transform(communities)
Q_t = np.vstack([np.zeros(embedding_dim), Q_t])
Q.append(Q_t)
return Q, subgraphs, labels, Q_t.shape Example 4
def grid_data(source, grid=32, crop=16, expand=12):
gridsize = grid + 2 * expand
stacksize = source.shape[0]
height = source.shape[3] # should be 224 for our data
width = source.shape[4]
gridheight = (height - 2 * crop) // grid # should be 6 for our data
gridwidth = (width - 2 * crop) // grid
cells = []
for j in range(gridheight):
for i in range (gridwidth):
cell = source[:,:,:, crop+j*grid-expand:crop+(j+1)*grid+expand, crop+i*grid-expand:crop+(i+1)*grid+expand]
cells.append(cell)
cells = np.vstack (cells)
return cells, gridwidth, gridheight Example 5
def calculate_beta(self):
"""
.. math::
\\beta_a = \\frac{\mathrm{Cov}(r_a,r_p)}{\mathrm{Var}(r_p)}
http://en.wikipedia.org/wiki/Beta_(finance)
"""
# it doesn't make much sense to calculate beta for less than two
# values, so return none.
if len(self.algorithm_returns) < 2:
return 0.0
returns_matrix = np.vstack([self.algorithm_returns,
self.benchmark_returns])
C = np.cov(returns_matrix, ddof=1)
algorithm_covariance = C[0][1]
benchmark_variance = C[1][1]
beta = algorithm_covariance / benchmark_variance
return beta Example 6
def KeyGen(**kwargs): ''' Appendix B of BLISS paper m_bar = m + n o/p: A: Public Key n x m' numpy array S: Secret Key m'x n numpy array ''' q, n, m, alpha = kwargs['q'], kwargs['n'], kwargs['m'], kwargs['alpha'] Aq_bar = util.crypt_secure_matrix(-(q-1)/2, (q-1)/2, n, m) S_bar = util.crypt_secure_matrix(-(2)**alpha, (2)**alpha, m, n) # alpha is small enough, we need not reduce (modq) S = np.vstack((S_bar, np.eye(n, dtype = int))) # dimension is m_bar x n, Elements are in Z mod(2q) A = np.hstack((2*Aq_bar, q * np.eye(n, dtype = int) - 2*np.matmul(Aq_bar,S_bar))) # dimension is n x m_bar , Elements are in Z mod(2q) #return util.matrix_to_Zq(A, 2*q), S, Aq_bar, S_bar return util.matrix_to_Zq(A, 2*q), S
Example 7
def main():
fish = loadmat('./data/fish.mat')
X1 = np.zeros((fish['X'].shape[0], fish['X'].shape[1] + 1))
X1[:,:-1] = fish['X']
X2 = np.ones((fish['X'].shape[0], fish['X'].shape[1] + 1))
X2[:,:-1] = fish['X']
X = np.vstack((X1, X2))
Y1 = np.zeros((fish['Y'].shape[0], fish['Y'].shape[1] + 1))
Y1[:,:-1] = fish['Y']
Y2 = np.ones((fish['Y'].shape[0], fish['Y'].shape[1] + 1))
Y2[:,:-1] = fish['Y']
Y = np.vstack((Y1, Y2))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
callback = partial(visualize, ax=ax)
reg = affine_registration(X, Y)
reg.register(callback)
plt.show() Example 8
def volume_to_point_cloud(vol):
""" vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize
return Nx3 numpy array.
"""
vsize = vol.shape[0]
assert(vol.shape[1] == vsize and vol.shape[1] == vsize)
points = []
for a in range(vsize):
for b in range(vsize):
for c in range(vsize):
if vol[a,b,c] == 1:
points.append(np.array([a,b,c]))
if len(points) == 0:
return np.zeros((0,3))
points = np.vstack(points)
return points
# ----------------------------------------
# Point cloud IO
# ---------------------------------------- Example 9
def standard_case(self):
"""Create standard testcase from Thetas defined in this Testcase. The following
metrics can be calculated by hand and should match the computations:
precisions: [1, 1, 0, 2/3, 1]
recalls: [1, 1, 0, 1, 0.5]
f1s: [1, 1, 0, 0.8, 2/3]
tps: 1 + 1 + 0 + 2 + 1 = 5
fps: 0 + 0 + 1 + 1 + 0 = 2
fns: 0 + 0 + 2 + 0 + 1 = 3
tns: 2 + 2 + 0 + 0 + 1 = 5
"""
Theta_true = np.vstack([
np.repeat(self.Theta_true1[nx, :, :], 2, axis=0),
np.repeat(self.Theta_true2[nx, :, :], 3, axis=0)
])
Theta_pred = np.vstack([
np.repeat(self.Theta_pred1[nx, :, :], 3, axis=0),
self.Theta_pred2[nx, :, :],
self.Theta_pred3[nx, :, :]
])
return Theta_true, Theta_pred Example 10
def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
""" A wrapper function to generate anchors given different scales
Also return the number of anchors in variable 'length'
"""
anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
A = anchors.shape[0]
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
K = shifts.shape[0]
# width changes faster, so here it is H, W, C
anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
length = np.int32(anchors.shape[0])
return anchors, length Example 11
def draw_laser_frustum(pose, zmin=0.0, zmax=10, fov=np.deg2rad(60)):
N = 30
curve = np.vstack([(
RigidTransform.from_rpyxyz(0, 0, rad, 0, 0, 0) * np.array([[zmax, 0, 0]]))
for rad in np.linspace(-fov/2, fov/2, N)])
curve_w = pose * curve
faces, edges = [], []
for cpt1, cpt2 in zip(curve_w[:-1], curve_w[1:]):
faces.extend([pose.translation, cpt1, cpt2])
edges.extend([cpt1, cpt2])
# Connect the last pt in the curve w/ the current pose,
# then connect the the first pt in the curve w/ the curr. pose
edges.extend([edges[-1], pose.translation])
edges.extend([edges[0], pose.translation])
faces = np.vstack(faces)
edges = np.vstack(edges)
return (faces, edges) Example 12
def __init__(self, target, instance, files):
self.target = target
self.instance = instance
mask_files = natural_sort(filter(lambda fn: '_maskcrop.png' in fn, files))
depth_files = natural_sort(filter(lambda fn: '_depthcrop.png' in fn, files))
rgb_files = natural_sort(list(set(files) - set(mask_files) - set(depth_files)))
loc_files = natural_sort(map(lambda fn: fn.replace('_crop.png', '_loc.txt'), rgb_files))
# Ensure all have equal number of files (Hack! doesn't ensure filename consistency)
nfiles = np.min([len(loc_files), len(mask_files), len(depth_files), len(rgb_files)])
mask_files, depth_files, rgb_files, loc_files = mask_files[:nfiles], depth_files[:nfiles], \
rgb_files[:nfiles], loc_files[:nfiles]
# print target, instance, len(loc_files), len(mask_files), len(depth_files), len(rgb_files)
assert(len(mask_files) == len(depth_files) == len(rgb_files) == len(loc_files))
# Read images
self.rgb = ImageDatasetReader.from_filenames(rgb_files)
self.depth = ImageDatasetReader.from_filenames(depth_files)
self.mask = ImageDatasetReader.from_filenames(mask_files)
# Read top-left locations of bounding box
self.locations = np.vstack([np.loadtxt(loc, delimiter=',', dtype=np.int32)
for loc in loc_files]) Example 13
def __init__(self, frame=None):
"""
Load annotation from json
"""
self.annotations_ = []
# Retrieve polygons
try:
polygons = frame['polygon']
except:
return
# For each polygon
for poly in polygons:
# Get coordinates
xy = np.vstack([np.float32(poly['x']),
np.float32(poly['y'])]).T
# Object ID (from local annotation file)
object_id = poly['object']
self.add(poly['object'], xy) Example 14
def im_detect_and_describe(img, mask=None, detector='dense', descriptor='SIFT', colorspace='gray',
step=4, levels=7, scale=np.sqrt(2)):
"""
Describe image using dense sampling / specific detector-descriptor combination.
"""
detector = get_detector(detector=detector, step=step, levels=levels, scale=scale)
extractor = cv2.DescriptorExtractor_create(descriptor)
try:
kpts = detector.detect(img, mask=mask)
kpts, desc = extractor.compute(img, kpts)
if descriptor == 'SIFT':
kpts, desc = root_sift(kpts, desc)
pts = np.vstack([kp.pt for kp in kpts]).astype(np.int32)
return pts, desc
except Exception as e:
print 'im_detect_and_describe', e
return None, None Example 15
def im_describe(*args, **kwargs):
"""
Describe image using dense sampling / specific detector-descriptor combination.
Sugar for description-only call.
"""
kpts, desc = im_detect_and_describe(*args, **kwargs)
return desc
# def color_codes(img, kpts):
# # Extract color information (Lab)
# pts = np.vstack([kp.pt for kp in kpts]).astype(np.int32)
# imgc = median_blur(img, size=5)
# cdesc = img[pts[:,1], pts[:,0]]
# return kpts, np.hstack([desc, cdesc])
# =====================================================================
# General-purpose object recognition interfaces, and functions
# --------------------------------------------------------------------- Example 16
def points_and_normals(self):
"""
Returns the point/normals parametrization for planes,
including clipped zmin and zmax frustums
Note: points need to be in CCW
"""
nv1, fv1 = self._front_back_vertices
nv2 = np.roll(nv1, -1, axis=0)
fv2 = np.roll(fv1, -1, axis=0)
vx = np.vstack([fv1-nv1, nv2[0]-nv1[0], fv1[2]-fv1[1]])
vy = np.vstack([fv2-fv1, nv2[1]-nv2[0], fv1[1]-fv1[0]])
pts = np.vstack([fv1, nv1[0], fv1[1]])
# vx += 1e-12
# vy += 1e-12
vx /= np.linalg.norm(vx, axis=1).reshape(-1,1)
vy /= np.linalg.norm(vy, axis=1).reshape(-1,1)
normals = np.cross(vx, vy)
normals /= np.linalg.norm(normals, axis=1).reshape(-1,1)
return pts, normals Example 17
def draw_bboxes(vis, bboxes, texts=None, ellipse=False, colored=True):
if not len(bboxes):
return vis
if not colored:
cols = np.tile([240,240,240], [len(bboxes), 1])
else:
N = 20
cwheel = colormap(np.linspace(0, 1, N))
cols = np.vstack([cwheel[idx % N] for idx, _ in enumerate(bboxes)])
texts = [None] * len(bboxes) if texts is None else texts
for col, b, t in zip(cols, bboxes, texts):
if ellipse:
cv2.ellipse(vis, ((b[0]+b[2])/2, (b[1]+b[3])/2), ((b[2]-b[0])/2, (b[3]-b[1])/2), 0, 0, 360,
color=tuple(col), thickness=1)
else:
cv2.rectangle(vis, (b[0], b[1]), (b[2], b[3]), tuple(col), 2)
if t:
annotate_bbox(vis, b, title=t)
return vis Example 18
def plotTimeMultiHistogram(parseTimes, hashTimes, compileTimes, filename): # times in ms
bins = np.linspace(0, 5000, 50)
data = np.vstack([parseTimes, hashTimes, compileTimes]).T
fig, ax = plt.subplots()
plt.hist(data, bins, alpha=0.7, label=['parsing', 'hashing', 'compiling'], color=[parseColor, hashColor, compileColor])
plt.legend(loc='upper right')
plt.xlabel('time [ms]')
plt.ylabel('#files')
fig.savefig(filename)
fig, ax = plt.subplots()
boxplot_data = [[i/1000 for i in parseTimes], [i/1000 for i in hashTimes], [i/1000 for i in compileTimes]] # times to s
plt.boxplot(boxplot_data, 0, 'rs', 0, [5, 95])
plt.xlabel('time [s]')
plt.yticks([1, 2, 3], ['parsing', 'hashing', 'compiling'])
#lgd = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # legend on the right
fig.savefig(filename[:-4] + '_boxplots' + GRAPH_EXTENSION) Example 19
def take_product(do_dict):
'''
this function takes some dictionary like:
{key1:1, key2:[a,b], key3:[c,d]}
and returns the dictionary:
{key1:[1,1,1], key2[a,a,b,b,],key3[c,d,c,d]}
computing the product of values
'''
values=[]
for v in do_dict.values():
if hasattr(v,'__iter__'):
values.append(v)
else:
values.append([v])#allows scalar to be passed
prod_values=np.vstack(product(*values))
return {k:np.array(v) for k,v in zip(do_dict.keys(),zip(*prod_values))} Example 20
def get_interv_table(model,intrv=True):
n_batches=25
table_outputs=[]
d_vals=np.linspace(TINY,0.6,n_batches)
for name in model.cc.node_names:
outputs=[]
for d_val in d_vals:
do_dict={model.cc.node_dict[name].label_logit : d_val*np.ones((model.batch_size,1))}
outputs.append(model.sess.run(model.fake_labels,do_dict))
out=np.vstack(outputs)
table_outputs.append(out)
table=np.stack(table_outputs,axis=2)
np.mean(np.round(table),axis=0)
return table
#dT=pd.DataFrame(index=p_names, data=T, columns=do_names)
#T=np.mean(np.round(table),axis=0)
#table=get_interv_table(model) Example 21
def __loadChnTimeWave(self,f,selectChan):
times = list()
waveforms = list()
spk_startswith = "spike_{0}".format(selectChan)
for chn_unit in f["spikes"].keys():
if chn_unit.startswith(spk_startswith):
time = f["spikes"][chn_unit]["times"].value
waveform = f["spikes"][chn_unit]["waveforms"].value
times.append(time)
waveforms.append(waveform)
if times:
times = np.hstack(times)
waveforms = np.vstack(waveforms)
sort_index = np.argsort(times)
waveforms = waveforms[sort_index]
times = times[sort_index]
return times,waveforms
else:
return None,None Example 22
def __load_waveforms(self,selectChan,file_name):
spk_startswith = "spike_{0}".format(selectChan)
with hp.File(file_name,"r") as f:
times = list()
waveforms = list()
for chn_unit in f["spikes"].keys():
if chn_unit.startswith(spk_startswith):
tep_time = f["spikes"][chn_unit]["times"].value
waveform = f["spikes"][chn_unit]["waveforms"].value
times.append(tep_time)
waveforms.append(waveform)
if times:
times = np.hstack(times)
waveforms = np.vstack(waveforms)
sort_index = np.argsort(times)
waveforms = waveforms[sort_index]
return waveforms
else:
return None Example 23
def _extract_signals(self, data, metadata, lazy):
signal = None
if lazy and data.size > 0:
signal = AnalogSignal([],
units=self._determine_units(metadata),
sampling_period=metadata['dt']*pq.ms)
signal.lazy_shape = None
else:
arr = numpy.vstack(self._extract_array(data, channel_index)
for channel_index in range(metadata['first_index'], metadata['last_index'] + 1))
if len(arr) > 0:
signal = AnalogSignal(arr.T,
units=self._determine_units(metadata),
sampling_period=metadata['dt']*pq.ms)
if signal is not None:
signal.annotate(label=metadata["label"],
variable=metadata["variable"])
return signal Example 24
def __load_waveforms(self,selectChan,file_name):
spk_startswith = "spike_{0}".format(selectChan)
with hp.File(file_name,"r") as f:
times = list()
waveforms = list()
for chn_unit in f["spikes"].keys():
if chn_unit.startswith(spk_startswith):
tep_time = f["spikes"][chn_unit]["times"].value
waveform = f["spikes"][chn_unit]["waveforms"].value
times.append(tep_time)
waveforms.append(waveform)
if times:
times = np.hstack(times)
waveforms = np.vstack(waveforms)
sort_index = np.argsort(times)
waveforms = waveforms[sort_index]
return waveforms
else:
return None Example 25
def _extract_signals(self, data, metadata, lazy):
signal = None
if lazy and data.size > 0:
signal = AnalogSignal([],
units=self._determine_units(metadata),
sampling_period=metadata['dt']*pq.ms)
signal.lazy_shape = None
else:
arr = numpy.vstack(self._extract_array(data, channel_index)
for channel_index in range(metadata['first_index'], metadata['last_index'] + 1))
if len(arr) > 0:
signal = AnalogSignal(arr.T,
units=self._determine_units(metadata),
sampling_period=metadata['dt']*pq.ms)
if signal is not None:
signal.annotate(label=metadata["label"],
variable=metadata["variable"])
return signal Example 26
def predict_proba(self, X):
try:
rows=(X.shape[0])
except:
rows=len(X)
X1 = self.build_matrix(X)
if self.k_models!=None and len(self.k_models)<2:
predictions = self.bst.predict(X1)
else :
dtest = xgb.DMatrix(X)
predictions= None
for gbdt in self.k_models:
predsnew = gbdt.predict(dtest, ntree_limit=(gbdt.best_iteration+1)*self.num_parallel_tree)
if predictions==None:
predictions=predsnew
else:
for g in range (0, predsnew.shape[0]):
predictions[g]+=predsnew[g]
for g in range (0, len(predictions)):
predictions[g]/=float(len(self.k_models))
predictions=np.array(predictions)
if self.objective == 'multi:softprob': return predictions.reshape( rows, self.num_class)
return np.vstack([1 - predictions, predictions]).T Example 27
def parse_audio_files(parent_dir,sub_dirs,file_ext='*.wav'):
ignored = 0
features, labels, name = np.empty((0,161)), np.empty(0), np.empty(0)
for label, sub_dir in enumerate(sub_dirs):
print sub_dir
for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):
try:
mfccs, chroma, mel, contrast, tonnetz = extract_features(fn)
ext_features = np.hstack([mfccs, chroma, mel, contrast, tonnetz])
features = np.vstack([features,ext_features])
l = [fn.split('-')[1]] * (mfccs.shape[0])
labels = np.append(labels, l)
except (KeyboardInterrupt, SystemExit):
raise
except:
ignored += 1
print "Ignored files: ", ignored
return np.array(features), np.array(labels, dtype = np.int) Example 28
def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
im_shapes = np.zeros((0, 2), dtype=np.float32)
for i in xrange(num_images):
im = cv2.imread(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale, im_shape = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size)
im_scales.append(im_scale)
processed_ims.append(im)
im_shapes = np.vstack((im_shapes, im_shape))
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales, im_shapes Example 29
def _block2df(block,obstypes,svnames,svnum):
"""
input: block of text corresponding to one time increment INTERVAL of RINEX file
output: 2-D array of float64 data from block. Future: consider whether best to use Numpy, Pandas, or Xray.
"""
nobs = len(obstypes)
stride=3
strio = BytesIO(block.encode())
barr = np.genfromtxt(strio, delimiter=(14,1,1)*5).reshape((svnum,-1), order='C')
data = barr[:,0:nobs*stride:stride]
lli = barr[:,1:nobs*stride:stride]
ssi = barr[:,2:nobs*stride:stride]
data = np.vstack(([data.T],[lli.T],[ssi.T])).T
return data Example 30
def _block2df(block,obstypes,svnames,svnum):
"""
input: block of text corresponding to one time increment INTERVAL of RINEX file
output: 2-D array of float64 data from block. Future: consider whether best to use Numpy, Pandas, or Xray.
"""
nobs = len(obstypes)
stride=3
strio = BytesIO(block.encode())
barr = np.genfromtxt(strio, delimiter=(14,1,1)*5).reshape((svnum,-1), order='C')
data = barr[:,0:nobs*stride:stride]
lli = barr[:,1:nobs*stride:stride]
ssi = barr[:,2:nobs*stride:stride]
data = np.vstack(([data.T],[lli.T],[ssi.T])).T
return data Example 31
def _block2df(block,obstypes,svnames,svnum):
"""
input: block of text corresponding to one time increment INTERVAL of RINEX file
output: 2-D array of float64 data from block.
"""
nobs = len(obstypes)
stride=3
strio = BytesIO(block.encode())
barr = np.genfromtxt(strio, delimiter=(14,1,1)*5).reshape((svnum,-1), order='C')
data = barr[:,0:nobs*stride:stride]
lli = barr[:,1:nobs*stride:stride]
ssi = barr[:,2:nobs*stride:stride]
data = np.vstack(([data],[lli],[ssi])).T #4D numpy array
return data Example 32
def get_score_bounds_from_range(Z_min, Z_max, rho_lb, rho_ub, L0_max = None):
"global variables: L0_reg_ind"
edge_values = np.vstack([Z_min * rho_lb,
Z_max * rho_lb,
Z_min * rho_ub,
Z_max * rho_ub])
if L0_max is None or L0_max == Z_min.shape[0]:
s_min = np.sum(np.min(edge_values, axis = 0))
s_max = np.sum(np.max(edge_values, axis = 0))
else:
min_values = np.min(edge_values, axis = 0)
s_min_reg = np.sum(np.sort(min_values[L0_reg_ind])[0:L0_max])
s_min_no_reg = np.sum(min_values[~L0_reg_ind])
s_min = s_min_reg + s_min_no_reg
max_values = np.max(edge_values, axis = 0)
s_max_reg = np.sum(-np.sort(-max_values[L0_reg_ind])[0:L0_max])
s_max_no_reg = np.sum(max_values[~L0_reg_ind])
s_max = s_max_reg + s_max_no_reg
return s_min, s_max
#setup weights Example 33
def get_score_bounds(Z_min, Z_max, rho_lb, rho_ub, L0_reg_ind = None, L0_max = None):
edge_values = np.vstack([Z_min * rho_lb,
Z_max * rho_lb,
Z_min * rho_ub,
Z_max * rho_ub])
if (L0_max is None) or (L0_reg_ind is None) or (L0_max == Z_min.shape[0]):
s_min = np.sum(np.min(edge_values, axis=0))
s_max = np.sum(np.max(edge_values, axis=0))
else:
min_values = np.min(edge_values, axis=0)
s_min_reg = np.sum(np.sort(min_values[L0_reg_ind])[0:L0_max])
s_min_no_reg = np.sum(min_values[~L0_reg_ind])
s_min = s_min_reg + s_min_no_reg
max_values = np.max(edge_values, axis=0)
s_max_reg = np.sum(-np.sort(-max_values[L0_reg_ind])[0:L0_max])
s_max_no_reg = np.sum(max_values[~L0_reg_ind])
s_max = s_max_reg + s_max_no_reg
return s_min, s_max Example 34
def _makeflat(self, start=None, end=None, groups = False):
eeg = list()
for sub in self.data[start:end]:
if len(sub) % self.chunk_len == 0:
eeg.append(sub.reshape([-1, self.chunk_len,3]))
else:
print('ERROR: Please choose a chunk length that is a factor of {}. Current len = {}'.format(self.samples_per_epoch, len(sub)))
return [0,0]
hypno = list()
group = list()
hypno_repeat = self.samples_per_epoch / self.chunk_len
idx = 0
for sub in self.hypno[start:end]:
hypno.append(np.repeat(sub, hypno_repeat))
group.append(np.repeat(idx, len(hypno[-1])))
idx += 1
if groups:
return np.vstack(eeg), np.hstack(hypno), np.hstack(group)
else:
return np.vstack(eeg), np.hstack(hypno) Example 35
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_)]
#%%
#l=a['feat_eeg']
#val_acc = [y[0] for y in [x for x in l]]
#val_f1 = [y[1] for y in [x for x in l]]
#test_acc = [y[2] for y in [x for x in l]]
#test_f1 = [y[3] for y in [x for x in l]]
#
#val = np.vstack([val_acc, val_f1]).T
#test = np.vstack([test_acc, test_f1]).T
#a = pickle.load(open('./results_dataset_feat_edfx','rb')) Example 36
def enroll(self, enroll_features):
"""enroll(enroll_features) -> model
Enrolls the model by storing all given input vectors.
**Parameters:**
enroll_features : [1D :py:class:`numpy.ndarray`]
The list of projected features to enroll the model from.
**Returns:**
model : 2D :py:class:`numpy.ndarray`
The enrolled model.
"""
assert len(enroll_features)
[self._check_feature(feature, True) for feature in enroll_features]
# just store all the features
return numpy.vstack(enroll_features) Example 37
def enroll(self, enroll_features):
"""enroll(enroll_features) -> model
Enrolls the model by storing all given input vectors.
**Parameters:**
``enroll_features`` : [:py:class:`numpy.ndarray`]
The list of projected features to enroll the model from.
**Returns:**
``model`` : 2D :py:class:`numpy.ndarray`
The enrolled model.
"""
assert len(enroll_features)
[self._check_feature(feature) for feature in enroll_features]
# just store all the features
return numpy.vstack(f.flatten() for f in enroll_features) Example 38
def train_projector(self, training_features, projector_file):
"""Generates the PCA covariance matrix and writes it into the given projector_file.
**Parameters:**
training_features : [1D :py:class:`numpy.ndarray`]
A list of 1D training arrays (vectors) to train the PCA projection matrix with.
projector_file : str
A writable file, into which the PCA projection matrix (as a :py:class:`bob.learn.linear.Machine`) and the eigenvalues will be written.
"""
# Assure that all data are 1D
[self._check_feature(feature) for feature in training_features]
# Initializes the data
data = numpy.vstack(training_features)
logger.info(" -> Training LinearMachine using PCA")
t = bob.learn.linear.PCATrainer()
self.machine, self.variances = t.train(data)
# For re-shaping, we need to copy...
self.variances = self.variances.copy()
# compute variance percentage, if desired
if isinstance(self.subspace_dim, float):
cummulated = numpy.cumsum(self.variances) / numpy.sum(self.variances)
for index in range(len(cummulated)):
if cummulated[index] > self.subspace_dim:
self.subspace_dim = index
break
self.subspace_dim = index
logger.info(" ... Keeping %d PCA dimensions", self.subspace_dim)
# re-shape machine
self.machine.resize(self.machine.shape[0], self.subspace_dim)
self.variances.resize(self.subspace_dim)
f = bob.io.base.HDF5File(projector_file, "w")
f.set("Eigenvalues", self.variances)
f.create_group("Machine")
f.cd("/Machine")
self.machine.save(f) Example 39
def enroll(self, enroll_features):
"""enroll(enroll_features) -> model
Enrolls the model by storing all given input vectors.
**Parameters:**
enroll_features : [1D :py:class:`numpy.ndarray`]
The list of projected features to enroll the model from.
**Returns:**
model : 2D :py:class:`numpy.ndarray`
The enrolled model.
"""
assert len(enroll_features)
[self._check_feature(feature, True) for feature in enroll_features]
# just store all the features
return numpy.vstack(enroll_features) Example 40
def _scores_d_normalize(t_model_ids, group):
"""Compute normalized D scores for the given T-model ids"""
# the file selector object
fs = FileSelector.instance()
# initialize D and D_same_value matrices
d_for_all = None
d_same_value = None
for t_model_id in t_model_ids:
tmp = bob.io.base.load(fs.d_file(t_model_id, group))
tmp2 = bob.io.base.load(fs.d_same_value_file(t_model_id, group))
if d_for_all is None and d_same_value is None:
d_for_all = tmp
d_same_value = tmp2
else:
d_for_all = numpy.vstack((d_for_all, tmp))
d_same_value = numpy.vstack((d_same_value, tmp2))
# Saves to files
bob.io.base.save(d_for_all, fs.d_matrix_file(group))
bob.io.base.save(d_same_value, fs.d_same_value_matrix_file(group)) Example 41
def x_frame2D(X, plot_limits=None, resolution=None):
"""
Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
"""
assert X.shape[1] == 2, \
'x_frame2D is defined for two-dimensional inputs'
if plot_limits is None:
(xmin, xmax) = (X.min(0), X.max(0))
(xmin, xmax) = (xmin - 0.2 * (xmax - xmin), xmax + 0.2 * (xmax
- xmin))
elif len(plot_limits) == 2:
(xmin, xmax) = plot_limits
else:
raise ValueError, 'Bad limits for plotting'
resolution = resolution or 50
(xx, yy) = np.mgrid[xmin[0]:xmax[0]:1j * resolution, xmin[1]:
xmax[1]:1j * resolution]
Xnew = np.vstack((xx.flatten(), yy.flatten())).T
return (Xnew, xx, yy, xmin, xmax) Example 42
def loadX(fname):
'''
Read data records into a data matrix.
Also return vocabulary.
'''
events = []
words_keys = set()
for e in load(fname):
events.append(e)
words_keys = words_keys | set(e[5].keys())
words_keys = sorted(list(words_keys))
for (eidx, e) in enumerate(events):
events[eidx] = list(e[:5]) + [e[5].get(word_key, 0)
for word_key in words_keys]
X = np.vstack(events)
return (X, words_keys) Example 43
def get_document_batch(self, doc_id):
"""builds batch of all mention pairs in one document
Args:
doc_id: id of document
Returns:
feature representation of mentions and labels
"""
mentions = self.dl.get_all_mentions_from_doc(doc_id)
if len(mentions) == 0:
return None, None
A, B = [], []
for a in mentions:
for b in mentions:
A.append(a)
B.append(b)
A_f = [self._mention_to_features(m) for m in A]
B_f = [self._mention_to_features(m) for m in B]
AB_f = self._pair_features(A, B)
A = [self.dl.mention_features[m] for m in A]
B = [self.dl.mention_features[m] for m in B]
return np.vstack(A), np.stack(A_f), np.vstack(B), np.stack(B_f), np.stack(AB_f) Example 44
def log(p): q = p.rot t = p.trans r = quat.log(q) D = quat.dlog(r) return np.vstack((r, D * t))
Example 45
def plotFields(layer,fieldShape=None,channel=None,figOffset=1,cmap=None,padding=0.01):
# Receptive Fields Summary
try:
W = layer.W
except:
W = layer
wp = W.eval().transpose();
if len(np.shape(wp)) < 4: # Fully connected layer, has no shape
fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape)
else: # Convolutional layer already has shape
features, channels, iy, ix = np.shape(wp)
if channel is not None:
fields = wp[:,channel,:,:]
else:
fields = np.reshape(wp,[features*channels,iy,ix])
perRow = int(math.floor(math.sqrt(fields.shape[0])))
perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
fig = mpl.figure(figOffset); mpl.clf()
# Using image grid
from mpl_toolkits.axes_grid1 import ImageGrid
grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
for i in range(0,np.shape(fields)[0]):
im = grid[i].imshow(fields[i],cmap=cmap);
grid.cbar_axes[0].colorbar(im)
mpl.title('%s Receptive Fields' % layer.name)
# old way
# fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
# tiled = []
# for i in range(0,perColumn*perRow,perColumn):
# tiled.append(np.hstack(fields2[i:i+perColumn]))
#
# tiled = np.vstack(tiled)
# mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar();
mpl.figure(figOffset+1); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar() Example 46
def plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None):
# Output summary
try:
W = layer.output
except:
W = layer
wp = W.eval(feed_dict=feed_dict);
if len(np.shape(wp)) < 4: # Fully connected layer, has no shape
temp = np.zeros(np.product(fieldShape)); temp[0:np.shape(wp.ravel())[0]] = wp.ravel()
fields = np.reshape(temp,[1]+fieldShape)
else: # Convolutional layer already has shape
wp = np.rollaxis(wp,3,0)
features, channels, iy,ix = np.shape(wp)
if channel is not None:
fields = wp[:,channel,:,:]
else:
fields = np.reshape(wp,[features*channels,iy,ix])
perRow = int(math.floor(math.sqrt(fields.shape[0])))
perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
tiled = []
for i in range(0,perColumn*perRow,perColumn):
tiled.append(np.hstack(fields2[i:i+perColumn]))
tiled = np.vstack(tiled)
if figOffset is not None:
mpl.figure(figOffset); mpl.clf();
mpl.imshow(tiled,cmap=cmap); mpl.title('%s Output' % layer.name); mpl.colorbar(); Example 47
def plotFields(layer,fieldShape=None,channel=None,maxFields=25,figName='ReceptiveFields',cmap=None,padding=0.01):
# Receptive Fields Summary
W = layer.W
wp = W.eval().transpose();
if len(np.shape(wp)) < 4: # Fully connected layer, has no shape
fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape)
else: # Convolutional layer already has shape
features, channels, iy, ix = np.shape(wp)
if channel is not None:
fields = wp[:,channel,:,:]
else:
fields = np.reshape(wp,[features*channels,iy,ix])
fieldsN = min(fields.shape[0],maxFields)
perRow = int(math.floor(math.sqrt(fieldsN)))
perColumn = int(math.ceil(fieldsN/float(perRow)))
fig = mpl.figure(figName); mpl.clf()
# Using image grid
from mpl_toolkits.axes_grid1 import ImageGrid
grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
for i in range(0,fieldsN):
im = grid[i].imshow(fields[i],cmap=cmap);
grid.cbar_axes[0].colorbar(im)
mpl.title('%s Receptive Fields' % layer.name)
# old way
# fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
# tiled = []
# for i in range(0,perColumn*perRow,perColumn):
# tiled.append(np.hstack(fields2[i:i+perColumn]))
#
# tiled = np.vstack(tiled)
# mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar();
mpl.figure(figName+' Total'); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar() Example 48
def plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None):
# Output summary
W = layer.output
wp = W.eval(feed_dict=feed_dict);
if len(np.shape(wp)) < 4: # Fully connected layer, has no shape
temp = np.zeros(np.product(fieldShape)); temp[0:np.shape(wp.ravel())[0]] = wp.ravel()
fields = np.reshape(temp,[1]+fieldShape)
else: # Convolutional layer already has shape
wp = np.rollaxis(wp,3,0)
features, channels, iy,ix = np.shape(wp)
if channel is not None:
fields = wp[:,channel,:,:]
else:
fields = np.reshape(wp,[features*channels,iy,ix])
perRow = int(math.floor(math.sqrt(fields.shape[0])))
perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
tiled = []
for i in range(0,perColumn*perRow,perColumn):
tiled.append(np.hstack(fields2[i:i+perColumn]))
tiled = np.vstack(tiled)
if figOffset is not None:
mpl.figure(figOffset); mpl.clf();
mpl.imshow(tiled,cmap=cmap); mpl.title('%s Output' % layer.name); mpl.colorbar(); Example 49
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 50
def get_label_batch(label_data, batch_size, batch_index):
nrof_examples = np.size(label_data, 0)
j = batch_index*batch_size % nrof_examples
if j+batch_size<=nrof_examples:
batch = label_data[j:j+batch_size]
else:
x1 = label_data[j:nrof_examples]
x2 = label_data[0:nrof_examples-j]
batch = np.vstack([x1,x2])
batch_int = batch.astype(np.int64)
return batch_int 