Python numpy.asmatrix() Examples
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Example #1
Source File: fisheye.py From DualFisheye with MIT License | 6 votes |
def add_pixels(self, uv_px, img1d, weight=None): # Lookup row & column for each in-bounds coordinate. mask = self.get_mask(uv_px) xx = uv_px[0,mask] yy = uv_px[1,mask] # Update matrix according to assigned weight. if weight is None: img1d[mask] = self.img[yy,xx] elif np.isscalar(weight): img1d[mask] += self.img[yy,xx] * weight else: w1 = np.asmatrix(weight, dtype='float32') w3 = w1.transpose() * np.ones((1,3)) img1d[mask] += np.multiply(self.img[yy,xx], w3[mask]) # A panorama image made from several FisheyeImage sources. # TODO: Add support for supersampled anti-aliasing filters.
Example #2
Source File: util.py From sfa-numpy with MIT License | 6 votes |
def coherence_of_columns(A): """Mutual coherence of columns of A. Parameters ---------- A : array_like Input matrix. p : int, optional p-th norm. Returns ------- array_like Mutual coherence of columns of A. """ A = np.asmatrix(A) _, N = A.shape A = A * np.asmatrix(np.diag(1/norm_of_columns(A))) Gram_A = A.H*A for j in range(N): Gram_A[j, j] = 0 return np.max(np.abs(Gram_A))
Example #3
Source File: label_ranker.py From chowmein with MIT License | 6 votes |
def label_relevance_score(self, topic_models, pmi_w2l): """ Calculate the relevance scores between each label and each topic Parameters: --------------- topic_models: numpy.ndarray(#topics, #words) the topic models pmi_w2l: numpy.ndarray(#words, #labels) the Point-wise Mutual Information(PMI) table of the form, PMI(w, l | C) Returns; ------------- numpy.ndarray, shape (#topics, #labels) the scores of each label on each topic """ assert topic_models.shape[1] == pmi_w2l.shape[0] return np.asarray(np.asmatrix(topic_models) * np.asmatrix(pmi_w2l))
Example #4
Source File: model.py From vadnet with GNU Lesser General Public License v3.0 | 6 votes |
def transform(info, sin, sout, sxtra, board, opts, vars): if vars['loaded']: sess = vars['sess'] x = vars['x'] y = vars['y'] ph_n_shuffle = vars['ph_n_shuffle'] ph_n_repeat = vars['ph_n_repeat'] ph_n_batch = vars['ph_n_batch'] init = vars['init'] logits = vars['logits'] input = np.asmatrix(sin).reshape(-1, x.shape[1]) dummy = np.zeros((input.shape[0],), dtype=np.int32) sess.run(init, feed_dict = { x : input, y : dummy, ph_n_shuffle : 1, ph_n_repeat : 1, ph_n_batch : input.shape[0] }) output = sess.run(logits) output = np.mean(output, axis=0) for i in range(sout.dim): sout[i] = output[i]
Example #5
Source File: test_shape_base.py From lambda-packs with MIT License | 6 votes |
def test_return_type(self): a = np.ones([2, 2]) m = np.asmatrix(a) assert_equal(type(kron(a, a)), np.ndarray) assert_equal(type(kron(m, m)), np.matrix) assert_equal(type(kron(a, m)), np.matrix) assert_equal(type(kron(m, a)), np.matrix) class myarray(np.ndarray): __array_priority__ = 0.0 ma = myarray(a.shape, a.dtype, a.data) assert_equal(type(kron(a, a)), np.ndarray) assert_equal(type(kron(ma, ma)), myarray) assert_equal(type(kron(a, ma)), np.ndarray) assert_equal(type(kron(ma, a)), myarray)
Example #6
Source File: vis.py From calfem-python with MIT License | 5 votes |
def _bspline(controlPoints, pointsOnCurve=20): ''' Uniform cubic B-spline. Params: controlPoints - Control points. Numpy array. One coordinate per row. pointsOnCurve - number of sub points per segment Mirrored start- and end-points are added if the curve is not closed. If the curve is closed some points are duplicated to make the closed spline continuous. (See http://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-curve-closed.html) Based on descriptions on: http://www.siggraph.org/education/materials/HyperGraph/modeling/splines/b_spline.htm http://en.wikipedia.org/wiki/B-spline#Uniform_cubic_B-splines ''' controlPoints = np.asarray(controlPoints) #Convert to array if input is a list. if (controlPoints[0,:] == controlPoints[-1,:]).all(): #If the curve is closed we extend each opposite endpoint to the other side CPs = np.asmatrix(np.vstack((controlPoints[-2,:], controlPoints, controlPoints[1,:]))) else:#Else make mirrored endpoints: CPs = np.asmatrix(np.vstack((2*controlPoints[0,:] - controlPoints[1,:], controlPoints, 2*controlPoints[-1,:] - controlPoints[-2,:]))) M = (1.0/6) * np.matrix([[-1, 3, -3, 1], [ 3, -6, 3, 0], [-3, 0, 3, 0], [ 1, 4, 1, 0]]) t = np.linspace(0, 1, pointsOnCurve) T = np.matrix([[pow(s,3), pow(s,2), s, 1] for s in t]) return np.asarray( np.vstack( T * M * CPs[i-1 : i+3, :] for i in range( 1, len(CPs)-2 ) ) )
Example #7
Source File: test_defmatrix.py From vnpy_crypto with MIT License | 5 votes |
def test_scalar_indexing(self): x = asmatrix(np.zeros((3, 2), float)) assert_equal(x[0, 0], x[0][0])
Example #8
Source File: test_defmatrix.py From vnpy_crypto with MIT License | 5 votes |
def test_asmatrix(self): A = np.arange(100).reshape(10, 10) mA = asmatrix(A) A[0, 0] = -10 assert_(A[0, 0] == mA[0, 0])
Example #9
Source File: pycalfem_vis.py From calfem-python with MIT License | 5 votes |
def _catmullspline(controlPoints, pointsOnEachSegment=10): """ Returns points on a Catmull-Rom spline that interpolated the control points. Inital/end tangents are created by mirroring the second/second-to-last) control points in the first/last points. Params: controlPoints - Numpy array containing the control points of the spline. Each row should contain the x,y,(z) values. [[x1, y2], [x2, y2], ... [xn, yn]] pointsOnEachSegment - The number of points on each segment of the curve. If there are n control points and k samplesPerSegment, then there will be (n+1)*k numeric points on the curve. """ controlPoints = np.asarray(controlPoints) #Convert to array if input is a list. if (controlPoints[0,:] == controlPoints[-1,:]).all(): #If the curve is closed we extend each opposite endpoint to the other side CPs = np.asmatrix(np.vstack((controlPoints[-2,:], controlPoints, controlPoints[1,:]))) else: #Else make mirrored endpoints: CPs = np.asmatrix(np.vstack((2*controlPoints[0,:] - controlPoints[1,:], controlPoints, 2*controlPoints[-1,:] - controlPoints[-2,:]))) M = 0.5 * np.matrix([[ 0, 2, 0, 0],[-1, 0, 1, 0],[ 2, -5, 4, -1],[-1, 3, -3, 1]]) t = np.linspace(0, 1, pointsOnEachSegment) T = np.matrix([[1, s, pow(s,2), pow(s,3)] for s in t]) return np.asarray( np.vstack( T * M * CPs[j-1:j+3,:] for j in range( 1, len(CPs)-2 ) ) )
Example #10
Source File: test_regression.py From vnpy_crypto with MIT License | 5 votes |
def test_matrix_std_argmax(self): # Ticket #83 x = np.asmatrix(np.random.uniform(0, 1, (3, 3))) assert_equal(x.std().shape, ()) assert_equal(x.argmax().shape, ())
Example #11
Source File: test_indexing.py From vnpy_crypto with MIT License | 5 votes |
def test_matrix_fancy(self): # The matrix class messes with the shape. While this is always # weird (getitem is not used, it does not have setitem nor knows # about fancy indexing), this tests gh-3110 m = np.matrix([[1, 2], [3, 4]]) assert_(isinstance(m[[0,1,0], :], np.matrix)) # gh-3110. Note the transpose currently because matrices do *not* # support dimension fixing for fancy indexing correctly. x = np.asmatrix(np.arange(50).reshape(5,10)) assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
Example #12
Source File: Transform.py From PyEngine3D with BSD 2-Clause "Simplified" License | 5 votes |
def transform(m, v): return np.asarray(m * np.asmatrix(v).T)[:, 0]
Example #13
Source File: matlib.py From Computable with MIT License | 5 votes |
def eye(n,M=None, k=0, dtype=float): """ Return a matrix with ones on the diagonal and zeros elsewhere. Parameters ---------- n : int Number of rows in the output. M : int, optional Number of columns in the output, defaults to `n`. k : int, optional Index of the diagonal: 0 refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal. dtype : dtype, optional Data-type of the returned matrix. Returns ------- I : matrix A `n` x `M` matrix where all elements are equal to zero, except for the `k`-th diagonal, whose values are equal to one. See Also -------- numpy.eye : Equivalent array function. identity : Square identity matrix. Examples -------- >>> import numpy.matlib >>> np.matlib.eye(3, k=1, dtype=float) matrix([[ 0., 1., 0.], [ 0., 0., 1.], [ 0., 0., 0.]]) """ return asmatrix(np.eye(n, M, k, dtype))
Example #14
Source File: test_defmatrix.py From Computable with MIT License | 5 votes |
def test_asmatrix(self): A = arange(100).reshape(10, 10) mA = asmatrix(A) A[0, 0] = -10 assert_(A[0, 0] == mA[0, 0])
Example #15
Source File: test_defmatrix.py From Computable with MIT License | 5 votes |
def test_scalar_indexing(self): x = asmatrix(zeros((3, 2), float)) assert_equal(x[0, 0], x[0][0])
Example #16
Source File: heston.py From tensortrade with Apache License 2.0 | 5 votes |
def get_correlated_geometric_brownian_motions(params: ModelParameters, correlation_matrix: np.array, n: int): """ Constructs a basket of correlated asset paths using the Cholesky decomposition method. Arguments: params : ModelParameters The parameters for the stochastic model. correlation_matrix : np.array An n x n correlation matrix. n : int Number of assets (number of paths to return) Returns: n correlated log return geometric brownian motion processes """ decomposition = sp.linalg.cholesky(correlation_matrix, lower=False) uncorrelated_paths = [] sqrt_delta_sigma = np.sqrt(params.all_delta) * params.all_sigma # Construct uncorrelated paths to convert into correlated paths for i in range(params.all_time): uncorrelated_random_numbers = [] for j in range(n): uncorrelated_random_numbers.append(random.normalvariate(0, sqrt_delta_sigma)) uncorrelated_paths.append(np.array(uncorrelated_random_numbers)) uncorrelated_matrix = np.asmatrix(uncorrelated_paths) correlated_matrix = uncorrelated_matrix * decomposition assert isinstance(correlated_matrix, np.matrix) # The rest of this method just extracts paths from the matrix extracted_paths = [] for i in range(1, n + 1): extracted_paths.append([]) for j in range(0, len(correlated_matrix) * n - n, n): for i in range(n): extracted_paths[i].append(correlated_matrix.item(j + i)) return extracted_paths
Example #17
Source File: test_defmatrix.py From vnpy_crypto with MIT License | 5 votes |
def test_list_indexing(self): A = np.arange(6) A.shape = (3, 2) x = asmatrix(A) assert_array_equal(x[:, [1, 0]], x[:, ::-1]) assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
Example #18
Source File: vis_mpl.py From calfem-python with MIT License | 5 votes |
def _catmullspline(controlPoints, pointsOnEachSegment=10): """ Returns points on a Catmull-Rom spline that interpolated the control points. Inital/end tangents are created by mirroring the second/second-to-last) control points in the first/last points. Params: controlPoints - Numpy array containing the control points of the spline. Each row should contain the x,y,(z) values. [[x1, y2], [x2, y2], ... [xn, yn]] pointsOnEachSegment - The number of points on each segment of the curve. If there are n control points and k samplesPerSegment, then there will be (n+1)*k numeric points on the curve. """ controlPoints = np.asarray( controlPoints) # Convert to array if input is a list. if (controlPoints[0, :] == controlPoints[-1, :]).all(): # If the curve is closed we extend each opposite endpoint to the other side CPs = np.asmatrix(np.vstack((controlPoints[-2, :], controlPoints, controlPoints[1, :]))) else: # Else make mirrored endpoints: CPs = np.asmatrix(np.vstack((2*controlPoints[0, :] - controlPoints[1, :], controlPoints, 2*controlPoints[-1, :] - controlPoints[-2, :]))) M = 0.5 * np.matrix([[0, 2, 0, 0], [-1, 0, 1, 0], [2, -5, 4, -1], [-1, 3, -3, 1]]) t = np.linspace(0, 1, pointsOnEachSegment) T = np.matrix([[1, s, pow(s, 2), pow(s, 3)] for s in t]) return np.asarray(np.vstack([T * M * CPs[j-1:j+3, :] for j in range(1, len(CPs)-2)]))
Example #19
Source File: matlib.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def eye(n,M=None, k=0, dtype=float): """ Return a matrix with ones on the diagonal and zeros elsewhere. Parameters ---------- n : int Number of rows in the output. M : int, optional Number of columns in the output, defaults to `n`. k : int, optional Index of the diagonal: 0 refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal. dtype : dtype, optional Data-type of the returned matrix. Returns ------- I : matrix A `n` x `M` matrix where all elements are equal to zero, except for the `k`-th diagonal, whose values are equal to one. See Also -------- numpy.eye : Equivalent array function. identity : Square identity matrix. Examples -------- >>> import numpy.matlib >>> np.matlib.eye(3, k=1, dtype=float) matrix([[ 0., 1., 0.], [ 0., 0., 1.], [ 0., 0., 0.]]) """ return asmatrix(np.eye(n, M, k, dtype))
Example #20
Source File: vis.py From calfem-python with MIT License | 5 votes |
def _catmullspline(controlPoints, pointsOnEachSegment=10): """ Returns points on a Catmull-Rom spline that interpolated the control points. Inital/end tangents are created by mirroring the second/second-to-last) control points in the first/last points. Params: controlPoints - Numpy array containing the control points of the spline. Each row should contain the x,y,(z) values. [[x1, y2], [x2, y2], ... [xn, yn]] pointsOnEachSegment - The number of points on each segment of the curve. If there are n control points and k samplesPerSegment, then there will be (n+1)*k numeric points on the curve. """ controlPoints = np.asarray(controlPoints) #Convert to array if input is a list. if (controlPoints[0,:] == controlPoints[-1,:]).all(): #If the curve is closed we extend each opposite endpoint to the other side CPs = np.asmatrix(np.vstack((controlPoints[-2,:], controlPoints, controlPoints[1,:]))) else: #Else make mirrored endpoints: CPs = np.asmatrix(np.vstack((2*controlPoints[0,:] - controlPoints[1,:], controlPoints, 2*controlPoints[-1,:] - controlPoints[-2,:]))) M = 0.5 * np.matrix([[ 0, 2, 0, 0],[-1, 0, 1, 0],[ 2, -5, 4, -1],[-1, 3, -3, 1]]) t = np.linspace(0, 1, pointsOnEachSegment) T = np.matrix([[1, s, pow(s,2), pow(s,3)] for s in t]) return np.asarray( np.vstack( T * M * CPs[j-1:j+3,:] for j in range( 1, len(CPs)-2 ) ) )
Example #21
Source File: test_regression.py From lambda-packs with MIT License | 5 votes |
def test_matrix_std_argmax(self,level=rlevel): # Ticket #83 x = np.asmatrix(np.random.uniform(0, 1, (3, 3))) self.assertEqual(x.std().shape, ()) self.assertEqual(x.argmax().shape, ())
Example #22
Source File: test_defmatrix.py From Computable with MIT License | 5 votes |
def test_row_column_indexing(self): x = asmatrix(np.eye(2)) assert_array_equal(x[0,:], [[1, 0]]) assert_array_equal(x[1,:], [[0, 1]]) assert_array_equal(x[:, 0], [[1], [0]]) assert_array_equal(x[:, 1], [[0], [1]])
Example #23
Source File: STFIWF.py From 2016CCF-sougou with Apache License 2.0 | 5 votes |
def inverse_transform(self, X): """Return terms per document with nonzero entries in X. Parameters ---------- X : {array, sparse matrix}, shape = [n_samples, n_features] Returns ------- X_inv : list of arrays, len = n_samples List of arrays of terms. """ self._check_vocabulary() if sp.issparse(X): # We need CSR format for fast row manipulations. X = X.tocsr() else: # We need to convert X to a matrix, so that the indexing # returns 2D objects X = np.asmatrix(X) n_samples = X.shape[0] terms = np.array(list(self.vocabulary_.keys())) indices = np.array(list(self.vocabulary_.values())) inverse_vocabulary = terms[np.argsort(indices)] return [inverse_vocabulary[X[i, :].nonzero()[1]].ravel() for i in range(n_samples)]
Example #24
Source File: test_indexing.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_matrix_fancy(self): # The matrix class messes with the shape. While this is always # weird (getitem is not used, it does not have setitem nor knows # about fancy indexing), this tests gh-3110 m = np.matrix([[1, 2], [3, 4]]) assert_(isinstance(m[[0,1,0], :], np.matrix)) # gh-3110. Note the transpose currently because matrices do *not* # support dimension fixing for fancy indexing correctly. x = np.asmatrix(np.arange(50).reshape(5,10)) assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
Example #25
Source File: test_regression.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_matrix_std_argmax(self,level=rlevel): # Ticket #83 x = np.asmatrix(np.random.uniform(0, 1, (3, 3))) self.assertEqual(x.std().shape, ()) self.assertEqual(x.argmax().shape, ())
Example #26
Source File: test_defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_list_indexing(self): A = np.arange(6) A.shape = (3, 2) x = asmatrix(A) assert_array_equal(x[:, [1, 0]], x[:, ::-1]) assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
Example #27
Source File: test_defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_boolean_indexing(self): A = np.arange(6) A.shape = (3, 2) x = asmatrix(A) assert_array_equal(x[:, np.array([True, False])], x[:, 0]) assert_array_equal(x[np.array([True, False, False]),:], x[0,:])
Example #28
Source File: test_defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_row_column_indexing(self): x = asmatrix(np.eye(2)) assert_array_equal(x[0,:], [[1, 0]]) assert_array_equal(x[1,:], [[0, 1]]) assert_array_equal(x[:, 0], [[1], [0]]) assert_array_equal(x[:, 1], [[0], [1]])
Example #29
Source File: test_defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_scalar_indexing(self): x = asmatrix(np.zeros((3, 2), float)) assert_equal(x[0, 0], x[0][0])
Example #30
Source File: test_defmatrix.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_asmatrix(self): A = np.arange(100).reshape(10, 10) mA = asmatrix(A) A[0, 0] = -10 assert_(A[0, 0] == mA[0, 0])