Here are the examples of the python api numpy.matlib.repmat taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
39 Examples
3
View Complete Implementation : test_matlib.py
Copyright MIT License
Author : abhisuri97
Copyright MIT License
Author : abhisuri97
def test_repmat():
a1 = np.arange(4)
x = numpy.matlib.repmat(a1, 2, 2)
y = np.array([[0, 1, 2, 3, 0, 1, 2, 3],
[0, 1, 2, 3, 0, 1, 2, 3]])
astert_array_equal(x, y)
3
View Complete Implementation : psl.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : aristoteleo
Copyright BSD 3-Clause "New" or "Revised" License
Author : aristoteleo
def repmat (X, m, n):
"""This function returns an array containing m (n) copies of A in the row (column) dimensions. The size of B is
size(A)*n when A is a matrix.For example, repmat(np.matrix(1:4), 2, 3) returns a 4-by-6 matrix.
Arguments
---------
X: 'np.ndarray'
An array like matrix.
m: 'int'
Number of copies on row dimension
n: 'int'
Number of copies on column dimension
Returns
-------
xy_rep: 'np.ndarray'
A matrix of repmat
"""
xy_rep = matlib.repmat(X, m, n)
return xy_rep
3
View Complete Implementation : environments.py
Copyright MIT License
Author : befelix
Copyright MIT License
Author : befelix
def _sample_start_state(self, mean=None, std=None, n_samples=1):
""" """
init_std = self.init_std
if not std is None:
init_std = std
init_m = mean
if init_m is None:
init_m = self.init_m
samples = (repmat(init_std, n_samples, 1) * np.random.randn(n_samples, self.n_s)
+ repmat(init_m, n_samples, 1))
return samples.T.squeeze()
3
View Complete Implementation : utils.py
Copyright MIT License
Author : befelix
Copyright MIT License
Author : befelix
def sample_inside_polytope(x, a, b):
"""
for a set of samples x = [x_1,..,x_k]^T
check sample_wise
Ax_i \leq b , i=1,..,k
x: k x n np.ndarray[float]
The samples (k samples of dimensionality n)
a: m x n np.ndarray[float]
the matrix of the linear inequality
b: m x 1 np.ndarray[float]
the vector of the linear inequality
"""
k, _ = x.shape
c = np.dot(a, x.T) - repmat(b, 1, k)
return np.all(c < 0, axis=0).squeeze()
3
View Complete Implementation : bayesian_logistic_regression.py
Copyright MIT License
Author : dilinwang820
Copyright MIT License
Author : dilinwang820
def evaluation(self, theta, X_test, y_test):
theta = theta[:, :-1]
M, n_test = theta.shape[0], len(y_test)
prob = np.zeros([n_test, M])
for t in range(M):
coff = np.multiply(y_test, np.sum(-1 * np.multiply(nm.repmat(theta[t, :], n_test, 1), X_test), axis=1))
prob[:, t] = np.divide(np.ones(n_test), (1 + np.exp(coff)))
prob = np.mean(prob, axis=1)
acc = np.mean(prob > 0.5)
llh = np.mean(np.log(prob))
return [acc, llh]
3
View Complete Implementation : util.py
Copyright GNU General Public License v3.0
Author : masabdi
Copyright GNU General Public License v3.0
Author : masabdi
def setNormSkel(self, norm_skel):
if len(norm_skel)%3 != 0:
raise ValueError('invalid length of the skeleton mat')
jntNum = len(norm_skel)/3
self.norm_skel = norm_skel.copy().astype(np.float32)
self.crop_skel = norm_skel.copy()*self.skel_norm_ratio
self.com3D = np.zeros([3])
self.com3D[2] = 200
self.skel = (self.crop_skel + repmat(self.com3D, 1, jntNum))[0]
self.skel = self.skel.astype(np.float32)
3
View Complete Implementation : util.py
Copyright GNU General Public License v3.0
Author : masabdi
Copyright GNU General Public License v3.0
Author : masabdi
def setCropSkel(self, crop_skel):
if len(crop_skel)%3 != 0:
raise ValueError('invalid length of the skeleton mat')
jntNum = len(crop_skel)/3
self.crop_skel = crop_skel.astype(np.float32)
self.skel = (self.crop_skel + repmat(self.com3D, 1, jntNum))[0]
self.skel = self.skel.astype(np.float32)
self.normSkel()
3
View Complete Implementation : util.py
Copyright GNU General Public License v3.0
Author : masabdi
Copyright GNU General Public License v3.0
Author : masabdi
def setSkel(self, skel):
if len(skel)%3 != 0:
raise ValueError('invalid length of the skeleton mat')
jntNum = len(skel)/3
self.skel = skel.astype(np.float32)
#crop_skel is the training label for neurual network, normalize wrt com3D
self.crop_skel = (self.skel - repmat(self.com3D, 1, jntNum))[0]
self.crop_skel = self.crop_skel.astype(np.float32)
self.normSkel()
3
View Complete Implementation : util.py
Copyright GNU General Public License v3.0
Author : masabdi
Copyright GNU General Public License v3.0
Author : masabdi
def crop2D(self):
self.crop_skel = self.norm_skel * np.float32(50.0)
skel = self.crop_skel.copy()
jntNum = len(skel)/3
skel = skel.reshape(-1, 3)
skel += repmat(self.com3D, jntNum, 1)
for i, jnt in enumerate(skel):
jnt = Camera.to2D(jnt)
pt = np.array([jnt[0],jnt[1], 1.0], np.float32).reshape(3,1)
pt = self.trans*pt
skel[i,0], skel[i,1] = pt[0], pt[1]
return skel
3
View Complete Implementation : pattern_clustering.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : mehrdadbakhtiari
Copyright BSD 3-Clause "New" or "Revised" License
Author : mehrdadbakhtiari
def get_elbow_point_index(wcss):
curve = wcss
number_of_points = len(curve)
all_coordinates = np.vstack((range(number_of_points), curve)).T
np.array([range(number_of_points), curve])
first_point = all_coordinates[0]
line_vector = all_coordinates[-1] - all_coordinates[0]
line_vector_norm = line_vector / np.sqrt(np.sum(line_vector**2))
vec_from_first = all_coordinates - first_point
scalar_product = np.sum(vec_from_first * matlib.repmat(line_vector_norm, number_of_points, 1), axis=1)
vec_from_first_parallel = np.outer(scalar_product, line_vector_norm)
vectors_to_line = vec_from_first - vec_from_first_parallel
dists_to_line = np.sqrt(np.sum(vectors_to_line ** 2, axis=1))
index_of_best_point = np.argmax(dists_to_line)
return index_of_best_point