Python numpy.vstack() Examples
The following are 30 code examples for showing how to use numpy.vstack(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: snippets.py License: MIT License | 6 votes |
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 2
Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 6 votes |
def mtx_updated_G(phi_recon, M, mtx_amp2visi_ri, mtx_fri2visi_ri): """ Update the linear transformation matrix that links the FRI sequence to the visibilities by using the reconstructed Dirac locations. :param phi_recon: the reconstructed Dirac locations (azimuths) :param M: the Fourier series expansion is between -M to M :param p_mic_x: a vector that contains microphones' x-coordinates :param p_mic_y: a vector that contains microphones' y-coordinates :param mtx_freq2visi: the linear mapping from Fourier series to visibilities :return: """ L = 2 * M + 1 ms_half = np.reshape(np.arange(-M, 1, step=1), (-1, 1), order='F') phi_recon = np.reshape(phi_recon, (1, -1), order='F') mtx_amp2freq = np.exp(-1j * ms_half * phi_recon) # size: (M + 1) x K mtx_amp2freq_ri = np.vstack((mtx_amp2freq.real, mtx_amp2freq.imag[:-1, :])) # size: (2M + 1) x K mtx_fri2amp_ri = linalg.lstsq(mtx_amp2freq_ri, np.eye(L))[0] # projection mtx_freq2visi to the null space of mtx_fri2amp mtx_null_proj = np.eye(L) - np.dot(mtx_fri2amp_ri.T, linalg.lstsq(mtx_fri2amp_ri.T, np.eye(L))[0]) G_updated = np.dot(mtx_amp2visi_ri, mtx_fri2amp_ri) + \ np.dot(mtx_fri2visi_ri, mtx_null_proj) return G_updated
Example 3
Project: neuropythy Author: noahbenson File: images.py License: GNU Affero General Public License v3.0 | 6 votes |
def image_reslice(image, spec, method=None, fill=0, dtype=None, weights=None, image_type=None): ''' image_reslice(image, spec) yields a duplicate of the given image resliced to have the voxels indicated by the given image spec. Note that spec may be an image itself. Optional arguments that can be passed to image_interpolate() (asside from affine) are allowed here and are passed through. ''' if image_type is None and is_image(image): image_type = to_image_type(image) spec = to_image_spec(spec) image = to_image(image) # we make a big mesh and interpolate at these points... imsh = spec['image_shape'] (args, kw) = ([np.arange(n) for n in imsh[:3]], {'indexing': 'ij'}) ijk = np.asarray([u.flatten() for u in np.meshgrid(*args, **kw)]) ijk = np.dot(spec['affine'], np.vstack([ijk, np.ones([1,ijk.shape[1]])]))[:3] # interpolate here... u = image_interpolate(image, ijk, method=method, fill=fill, dtype=dtype, weights=weights) return to_image((np.reshape(u, imsh), spec), image_type=image_type)
Example 4
Project: neuropythy Author: noahbenson File: models.py License: GNU Affero General Public License v3.0 | 6 votes |
def angle_to_cortex(self, theta, rho): 'See help(neuropythy.registration.RetinotopyModel.angle_to_cortex).' #TODO: This should be made to work correctly with visual area boundaries: this could be done # by, for each area (e.g., V2) looking at its boundaries (with V1 and V3) and flipping the # adjacent triangles so that there is complete coverage of each hemifield, guaranteed. if not pimms.is_vector(theta): return self.angle_to_cortex([theta], [rho])[0] theta = np.asarray(theta) rho = np.asarray(rho) zs = np.asarray( rho * np.exp([np.complex(z) for z in 1j * ((90.0 - theta)/180.0*np.pi)]), dtype=np.complex) coords = np.asarray([zs.real, zs.imag]).T if coords.shape[0] == 0: return np.zeros((0, len(self.visual_meshes), 2)) # we step through each area in the forward model and return the appropriate values tx = self.transform res = np.transpose( [self.visual_meshes[area].interpolate(coords, 'cortical_coordinates', method='linear') for area in sorted(self.visual_meshes.keys())], (1,0,2)) if tx is not None: res = np.asarray( [np.dot(tx, np.vstack((area_xy.T, np.ones(len(area_xy)))))[0:2].T for area_xy in res]) return res
Example 5
Project: neuropythy Author: noahbenson File: core.py License: GNU Affero General Public License v3.0 | 6 votes |
def apply_affine(aff, coords): ''' apply_affine(affine, coords) yields the result of applying the given affine transformation to the given coordinate or coordinates. This function expects coords to be a (dims X n) matrix but if the first dimension is neither 2 nor 3, coords.T is used; i.e.: apply_affine(affine3x3, coords2xN) ==> newcoords2xN apply_affine(affine4x4, coords3xN) ==> newcoords3xN apply_affine(affine3x3, coordsNx2) ==> newcoordsNx2 (for N != 2) apply_affine(affine4x4, coordsNx3) ==> newcoordsNx3 (for N != 3) ''' if aff is None: return coords (coords,tr) = (np.asanyarray(coords), False) if len(coords.shape) == 1: return np.squeeze(apply_affine(np.reshape(coords, (-1,1)), aff)) elif len(coords.shape) > 2: raise ValueError('cannot apply affine to ND-array for N > 2') if len(coords) == 2: aff = to_affine(aff, 2) elif len(coords) == 3: aff = to_affine(aff, 3) else: (coords,aff,tr) = (coords.T, to_affine(aff, coords.shape[1]), True) r = np.dot(aff, np.vstack([coords, np.ones([1,coords.shape[1]])]))[:-1] return r.T if tr else r
Example 6
Project: neuropythy Author: noahbenson File: core.py License: GNU Affero General Public License v3.0 | 6 votes |
def subcurve(self, t0, t1): ''' curve.subcurve(t0, t1) yields a curve-spline object that is equivalent to the given curve but that extends from curve(t0) to curve(t1) only. ''' # if t1 is less than t0, then we want to actually do this in reverse... if t1 == t0: raise ValueError('Cannot take subcurve of a point') if t1 < t0: tt = self.curve_length() return self.reverse().subcurve(tt - t0, tt - t1) idx = [ii for (ii,t) in enumerate(self.t) if t0 < t and t < t1] pt0 = self(t0) pt1 = self(t1) coords = np.vstack([[pt0], self.coordinates.T[idx], [pt1]]) ts = np.concatenate([[t0], self.t[idx], [t1]]) dists = None if self.distances is None else np.diff(ts) return CurveSpline( coords.T, order=self.order, smoothing=self.smoothing, periodic=False, distances=dists, meta_data=self.meta_data)
Example 7
Project: discomll Author: romanorac File: datasets.py License: Apache License 2.0 | 6 votes |
def breastcancer_cont(replication=2): f = open(path + "breast_cancer_wisconsin_cont.txt", "r") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_train = np.array(data[:, range(0, 9)]) y_train = np.array(data[:, 9]) for j in range(replication - 1): x_train = np.vstack([x_train, data[:, range(0, 9)]]) y_train = np.hstack([y_train, data[:, 9]]) x_train = np.array(x_train, dtype=np.float) f = open(path + "breast_cancer_wisconsin_cont_test.txt") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_test = np.array(data[:, range(0, 9)]) y_test = np.array(data[:, 9]) for j in range(replication - 1): x_test = np.vstack([x_test, data[:, range(0, 9)]]) y_test = np.hstack([y_test, data[:, 9]]) x_test = np.array(x_test, dtype=np.float) return x_train, y_train, x_test, y_test
Example 8
Project: discomll Author: romanorac File: datasets.py License: Apache License 2.0 | 6 votes |
def breastcancer_disc(replication=2): f = open(path + "breast_cancer_wisconsin_disc.txt") data = np.loadtxt(f, delimiter=",") x_train = data[:, range(1, 10)] y_train = data[:, 10] for j in range(replication - 1): x_train = np.vstack([x_train, data[:, range(1, 10)]]) y_train = np.hstack([y_train, data[:, 10]]) f = open(path + "breast_cancer_wisconsin_disc_test.txt") data = np.loadtxt(f, delimiter=",") x_test = data[:, range(1, 10)] y_test = data[:, 10] for j in range(replication - 1): x_test = np.vstack([x_test, data[:, range(1, 10)]]) y_test = np.hstack([y_test, data[:, 10]]) return x_train, y_train, x_test, y_test
Example 9
Project: discomll Author: romanorac File: datasets.py License: Apache License 2.0 | 6 votes |
def iris(replication=2): f = open(path + "iris.txt") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_train = np.array(data[:, range(0, 4)], dtype=np.float) y_train = data[:, 4] for j in range(replication - 1): x_train = np.vstack([x_train, data[:, range(0, 4)]]) y_train = np.hstack([y_train, data[:, 4]]) x_train = np.array(x_train, dtype=np.float) f = open(path + "iris_test.txt") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_test = np.array(data[:, range(0, 4)], dtype=np.float) y_test = data[:, 4] for j in range(replication - 1): x_test = np.vstack([x_test, data[:, range(0, 4)]]) y_test = np.hstack([y_test, data[:, 4]]) x_test = np.array(x_test, dtype=np.float) return x_train, y_train, x_test, y_test
Example 10
Project: discomll Author: romanorac File: datasets.py License: Apache License 2.0 | 6 votes |
def regression_data(): f = open(path + "regression_data1.txt") data = np.loadtxt(f, delimiter=",") x1 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y1 = data[:, 1] f = open(path + "regression_data2.txt") data = np.loadtxt(f, delimiter=",") x2 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y2 = data[:, 1] x1 = np.vstack((x1, x2)) y1 = np.hstack((y1, y2)) f = open(path + "regression_data_test1.txt") data = np.loadtxt(f, delimiter=",") x1_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y1_test = data[:, 1] f = open(path + "regression_data_test2.txt") data = np.loadtxt(f, delimiter=",") x2_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y2_test = data[:, 1] x1_test = np.vstack((x1_test, x2_test)) y1_test = np.hstack((y1_test, y2_test)) return x1, y1, x1_test, y1_test
Example 11
Project: discomll Author: romanorac File: datasets.py License: Apache License 2.0 | 6 votes |
def ex3(replication=2): f = open(path + "ex3.txt") train_data = np.loadtxt(f, delimiter=",") f = open(path + "ex3_test.txt") test_data = np.loadtxt(f, delimiter=",") x_train = np.insert(train_data[:, (0, 1)], 0, np.ones(len(train_data)), axis=1) y_train = train_data[:, 2] x_test = np.insert(test_data[:, (0, 1)], 0, np.ones(len(test_data)), axis=1) y_test = test_data[:, 2] for i in range(replication - 1): x_train = np.vstack((x_train, np.insert(train_data[:, (0, 1)], 0, np.ones(len(train_data)), axis=1))) y_train = np.hstack((y_train, train_data[:, 2])) x_test = np.vstack((x_test, np.insert(test_data[:, (0, 1)], 0, np.ones(len(test_data)), axis=1))) y_test = np.hstack((y_test, test_data[:, 2])) return x_train, y_train, x_test, y_test
Example 12
Project: transferlearning Author: jindongwang File: intra_alignment.py License: MIT License | 6 votes |
def getGFKDim(Xs, Xt): Pss = PCA().fit(Xs).components_.T Pts = PCA().fit(Xt).components_.T Psstt = PCA().fit(np.vstack((Xs, Xt))).components_.T DIM = round(Xs.shape[1]*0.5) res = -1 for d in range(1, DIM+1): Ps = Pss[:, :d] Pt = Pts[:, :d] Pst = Psstt[:, :d] alpha1 = getAngle(Ps, Pst, d) alpha2 = getAngle(Pt, Pst, d) D = (alpha1 + alpha2) * 0.5 check = [round(D[1, dd]*100) == 100 for dd in range(d)] if True in check: res = list(map(lambda i: i == True, check)).index(True) return res
Example 13
Project: transferlearning Author: jindongwang File: TCA.py License: MIT License | 6 votes |
def fit(self, Xs, Xt): ''' Transform Xs and Xt :param Xs: ns * n_feature, source feature :param Xt: nt * n_feature, target feature :return: Xs_new and Xt_new after TCA ''' X = np.hstack((Xs.T, Xt.T)) X /= np.linalg.norm(X, axis=0) m, n = X.shape ns, nt = len(Xs), len(Xt) e = np.vstack((1 / ns * np.ones((ns, 1)), -1 / nt * np.ones((nt, 1)))) M = e * e.T M = M / np.linalg.norm(M, 'fro') H = np.eye(n) - 1 / n * np.ones((n, n)) K = kernel(self.kernel_type, X, None, gamma=self.gamma) n_eye = m if self.kernel_type == 'primal' else n a, b = np.linalg.multi_dot([K, M, K.T]) + self.lamb * np.eye(n_eye), np.linalg.multi_dot([K, H, K.T]) w, V = scipy.linalg.eig(a, b) ind = np.argsort(w) A = V[:, ind[:self.dim]] Z = np.dot(A.T, K) Z /= np.linalg.norm(Z, axis=0) Xs_new, Xt_new = Z[:, :ns].T, Z[:, ns:].T return Xs_new, Xt_new
Example 14
Project: transferlearning Author: jindongwang File: BDA.py License: MIT License | 6 votes |
def proxy_a_distance(source_X, target_X): """ Compute the Proxy-A-Distance of a source/target representation """ nb_source = np.shape(source_X)[0] nb_target = np.shape(target_X)[0] train_X = np.vstack((source_X, target_X)) train_Y = np.hstack((np.zeros(nb_source, dtype=int), np.ones(nb_target, dtype=int))) clf = svm.LinearSVC(random_state=0) clf.fit(train_X, train_Y) y_pred = clf.predict(train_X) error = metrics.mean_absolute_error(train_Y, y_pred) dist = 2 * (1 - 2 * error) return dist
Example 15
Project: EXOSIMS Author: dsavransky File: coroOnlyScheduler.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def scheduleRevisit(self, sInd, smin, det, pInds): """A Helper Method for scheduling revisits after observation detection Args: sInd - sInd of the star just detected smin - minimum separation of the planet to star of planet just detected det - pInds - Indices of planets around target star Return: updates self.starRevisit attribute """ TK = self.TimeKeeping t_rev = TK.currentTimeNorm.copy() + self.revisit_wait[sInd] # finally, populate the revisit list (NOTE: sInd becomes a float) revisit = np.array([sInd, t_rev.to('day').value]) if self.starRevisit.size == 0:#If starRevisit has nothing in it self.starRevisit = np.array([revisit])#initialize sterRevisit else: revInd = np.where(self.starRevisit[:,0] == sInd)[0]#indices of the first column of the starRevisit list containing sInd if revInd.size == 0: self.starRevisit = np.vstack((self.starRevisit, revisit)) else: self.starRevisit[revInd,1] = revisit[1]#over
Example 16
Project: libTLDA Author: wmkouw File: test_tcpr.py License: MIT License | 5 votes |
def test_fit(): """Test for fitting the model.""" X = np.vstack((rnd.randn(5, 2), rnd.randn(5, 2)+1)) y = np.hstack((np.zeros((5,)), np.ones((5,)))) Z = np.vstack((rnd.randn(5, 2)-1, rnd.randn(5, 2)+2)) clf = TargetContrastivePessimisticClassifier(l2=0.1) clf.fit(X, y, Z) assert clf.is_trained
Example 17
Project: libTLDA Author: wmkouw File: test_tcpr.py License: MIT License | 5 votes |
def test_predict(): """Test for making predictions.""" X = np.vstack((rnd.randn(5, 2), rnd.randn(5, 2)+1)) y = np.hstack((np.zeros((5,)), np.ones((5,)))) Z = np.vstack((rnd.randn(5, 2)-1, rnd.randn(5, 2)+2)) clf = TargetContrastivePessimisticClassifier(l2=0.1) clf.fit(X, y, Z) u_pred = clf.predict(Z) labels = np.unique(y) assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
Example 18
Project: libTLDA Author: wmkouw File: test_scl.py License: MIT License | 5 votes |
def test_init(): """Test for object type.""" clf = StructuralCorrespondenceClassifier() assert type(clf) == StructuralCorrespondenceClassifier assert not clf.is_trained # def test_fit(): # """Test for fitting the model.""" # X = np.vstack((rnd.randn(5, 2), rnd.randn(5, 2)+1)) # y = np.hstack((np.zeros((5,)), np.ones((5,)))) # Z = np.vstack((rnd.randn(5, 2)-1, rnd.randn(5, 2)+2)) # clf = StructuralCorrespondenceClassifier(l2=1.0) # clf.fit(X, y, Z) # assert clf.is_trained # def test_predict(): # """Test for making predictions.""" # X = np.vstack((rnd.randn(5, 2), rnd.randn(5, 2)+1)) # y = np.hstack((np.zeros((5,)), np.ones((5,)))) # Z = np.vstack((rnd.randn(5, 2)-1, rnd.randn(5, 2)+2)) # clf = StructuralCorrespondenceClassifier(l2=1.0) # clf.fit(X, y, Z) # u_pred = clf.predict(Z) # labels = np.unique(y) # assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
Example 19
Project: libTLDA Author: wmkouw File: viz.py License: MIT License | 5 votes |
def plotc(parameters, ax=[], color='k', gridsize=(101, 101)): """ Plot a linear classifier in a 2D scatterplot. INPUT (1) tuple 'parameters': consists of a list of class proportions (1 by K classes), an array of class means (K classes by D features), an array of class-covariance matrices (D features by D features by K classes) (2) object 'ax': axes of a pyplot figure or subject (def: empty) (3) str 'colors': colors of the contours in the plot (def: 'k') (4) tuple 'gridsize': number of points in the grid (def: (101, 101)) OUTPUT None """ # Check for figure object if fig: ax = fig.gca() else: fig, ax = plt.subplots() # Get axes limits xl = ax.get_xlim() yl = ax.get_ylim() # Define grid gx = np.linspace(xl[0], xl[1], gridsize[0]) gy = np.linspace(yl[0], yl[1], gridsize[1]) x, y = np.meshgrid(gx, gy) xy = np.vstack((x.ravel(), y.ravel())).T # Values of grid z = np.dot(xy, parameters[:-1, :]) + parameters[-1, :] z = np.reshape(z[:, 0] - z[:, 1], gridsize) # Plot grid ax.contour(x, y, z, levels=0, colors=colors)
Example 20
Project: fenics-topopt Author: zfergus File: L_bracket.py License: MIT License | 5 votes |
def get_passive_elements(self): X, Y = np.mgrid[self.passive_min_x:self.passive_max_x + 1, self.passive_min_y:self.passive_max_y] pairs = np.vstack([X.ravel(), Y.ravel()]).T passive_to_ids = np.vectorize(lambda pair: xy_to_id(*pair, nelx=self.nelx - 1, nely=self.nely - 1), signature="(m)->()") return passive_to_ids(pairs)
Example 21
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: imdb.py License: MIT License | 5 votes |
def merge_roidbs(a, b): assert len(a) == len(b) for i in range(len(a)): a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes'])) a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'], b[i]['gt_classes'])) a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'], b[i]['gt_overlaps']]) a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'], b[i]['seg_areas'])) return a
Example 22
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: generate_anchors.py License: MIT License | 5 votes |
def generate_anchors(base_size=16, ratios=[0.5, 1, 2], scales=2 ** np.arange(3, 6)): """ Generate anchor (reference) windows by enumerating aspect ratios X scales wrt a reference (0, 0, 15, 15) window. """ base_anchor = np.array([1, 1, base_size, base_size]) - 1 ratio_anchors = _ratio_enum(base_anchor, ratios) anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales) for i in range(ratio_anchors.shape[0])]) return anchors
Example 23
Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 5 votes |
def cpx_mtx2real(mtx): """ extend complex valued matrix to an extended matrix of real values only :param mtx: input complex valued matrix :return: """ return np.vstack((np.hstack((mtx.real, -mtx.imag)), np.hstack((mtx.imag, mtx.real))))
Example 24
Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 5 votes |
def hermitian_expan(half_vec_len): """ expand a real-valued vector to a Hermitian symmetric vector. The input vector is a concatenation of the real parts with NON-POSITIVE indices and the imaginary parts with STRICTLY-NEGATIVE indices. :param half_vec_len: length of the first half vector :return: """ D0 = np.eye(half_vec_len) D1 = np.vstack((D0, D0[1:, ::-1])) D2 = np.vstack((D0, -D0[1:, ::-1])) D2 = D2[:, :-1] return D1, D2
Example 25
Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 5 votes |
def coef_expan_mtx(K): """ expansion matrix for an annihilating filter of size K + 1 :param K: number of Dirac. The filter size is K + 1 :return: """ if K % 2 == 0: D0 = np.eye(np.int(K / 2. + 1)) D1 = np.vstack((D0, D0[1:, ::-1])) D2 = np.vstack((D0, -D0[1:, ::-1]))[:, :-1] else: D0 = np.eye(np.int((K + 1) / 2.)) D1 = np.vstack((D0, D0[:, ::-1])) D2 = np.vstack((D0, -D0[:, ::-1])) return D1, D2
Example 26
Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 5 votes |
def Tmtx_ri(b_ri, K, D, L): """ build convolution matrix associated with b_ri :param b_ri: a real-valued vector :param K: number of Diracs :param D1: expansion matrix for the real-part :param D2: expansion matrix for the imaginary-part :return: """ b_ri = np.dot(D, b_ri) b_r = b_ri[:L] b_i = b_ri[L:] Tb_r = linalg.toeplitz(b_r[K:], b_r[K::-1]) Tb_i = linalg.toeplitz(b_i[K:], b_i[K::-1]) return np.vstack((np.hstack((Tb_r, -Tb_i)), np.hstack((Tb_i, Tb_r))))
Example 27
Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 5 votes |
def Rmtx_ri(coef_ri, K, D, L): coef_ri = np.squeeze(coef_ri) coef_r = coef_ri[:K + 1] coef_i = coef_ri[K + 1:] R_r = linalg.toeplitz(np.concatenate((np.array([coef_r[-1]]), np.zeros(L - K - 1))), np.concatenate((coef_r[::-1], np.zeros(L - K - 1))) ) R_i = linalg.toeplitz(np.concatenate((np.array([coef_i[-1]]), np.zeros(L - K - 1))), np.concatenate((coef_i[::-1], np.zeros(L - K - 1))) ) return np.dot(np.vstack((np.hstack((R_r, -R_i)), np.hstack((R_i, R_r)))), D)
Example 28
Project: FRIDA Author: LCAV File: tools_fri_doa_plane.py License: MIT License | 5 votes |
def mtx_updated_G_multiband(phi_recon, M, mtx_amp2visi_ri, mtx_fri2visi_ri, num_bands): """ Update the linear transformation matrix that links the FRI sequence to the visibilities by using the reconstructed Dirac locations. :param phi_recon: the reconstructed Dirac locations (azimuths) :param M: the Fourier series expansion is between -M to M :param p_mic_x: a vector that contains microphones' x-coordinates :param p_mic_y: a vector that contains microphones' y-coordinates :param mtx_freq2visi: the linear mapping from Fourier series to visibilities :return: """ L = 2 * M + 1 ms_half = np.reshape(np.arange(-M, 1, step=1), (-1, 1), order='F') phi_recon = np.reshape(phi_recon, (1, -1), order='F') mtx_amp2freq = np.exp(-1j * ms_half * phi_recon) # size: (M + 1) x K mtx_amp2freq_ri = np.vstack((mtx_amp2freq.real, mtx_amp2freq.imag[:-1, :])) # size: (2M + 1) x K mtx_fri2amp_ri = linalg.lstsq(mtx_amp2freq_ri, np.eye(L))[0] # projection mtx_freq2visi to the null space of mtx_fri2amp mtx_null_proj = np.eye(L) - np.dot(mtx_fri2amp_ri.T, linalg.lstsq(mtx_fri2amp_ri.T, np.eye(L))[0]) G_updated = np.dot(mtx_amp2visi_ri, linalg.block_diag(*([mtx_fri2amp_ri] * num_bands)) ) + \ np.dot(mtx_fri2visi_ri, linalg.block_diag(*([mtx_null_proj] * num_bands)) ) return G_updated
Example 29
Project: StructEngPy Author: zhuoju36 File: __init__.py License: MIT License | 5 votes |
def resolve_beam_force(self,beam_id): if not self.is_solved: raise Exception('The model has to be solved first.') if beam_id in self.__beams.keys(): beam=self.__beams[beam_id] i=beam.nodes[0].hid j=beam.nodes[1].hid T=beam.transform_matrix ue=np.vstack([ self.d_[i*6:i*6+6], self.d_[j*6:j*6+6] ]) return (beam.Ke_.dot(T.dot(ue))+beam.re_).reshape(12) else: raise Exception("The element doesn't exists.")
Example 30
Project: mmdetection Author: open-mmlab File: coco_error_analysis.py License: Apache License 2.0 | 5 votes |
def makeplot(rs, ps, outDir, class_name, iou_type): cs = np.vstack([ np.ones((2, 3)), np.array([.31, .51, .74]), np.array([.75, .31, .30]), np.array([.36, .90, .38]), np.array([.50, .39, .64]), np.array([1, .6, 0]) ]) areaNames = ['allarea', 'small', 'medium', 'large'] types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN'] for i in range(len(areaNames)): area_ps = ps[..., i, 0] figure_tile = iou_type + '-' + class_name + '-' + areaNames[i] aps = [ps_.mean() for ps_ in area_ps] ps_curve = [ ps_.mean(axis=1) if ps_.ndim > 1 else ps_ for ps_ in area_ps ] ps_curve.insert(0, np.zeros(ps_curve[0].shape)) fig = plt.figure() ax = plt.subplot(111) for k in range(len(types)): ax.plot(rs, ps_curve[k + 1], color=[0, 0, 0], linewidth=0.5) ax.fill_between( rs, ps_curve[k], ps_curve[k + 1], color=cs[k], label=str(f'[{aps[k]:.3f}]' + types[k])) plt.xlabel('recall') plt.ylabel('precision') plt.xlim(0, 1.) plt.ylim(0, 1.) plt.title(figure_tile) plt.legend() # plt.show() fig.savefig(outDir + f'/{figure_tile}.png') plt.close(fig)