numpy.transpose

Here are the examples of the python api numpy.transpose taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

3194 Examples 7

5 Source : test_tensor_sum.py
with MIT License
from alibaba

    def test_transpose(self):
        axesA = (2, 4, 0, 1, 3)
        axesB = (3, 4, 1, 0, 2)
        axesC = (3, 0, 1, 2, 4)
        axes_ = (1, 3, 0, 4, 2)
        c = (self.a % axesA
             + self.b % axesB
             + self.c % axesC) % axes_
        data = numpy.transpose(numpy.transpose(self.a.contract(), axesA)
                               + numpy.transpose(self.b.contract(), axesB)
                               + numpy.transpose(self.c.contract(), axesC), axes_)
        self.assertTrue(numpy.allclose(c.contract(), data))

    def test_add_and_remove_term(self):

3 Source : LinearRegression_NormalEquation.py
with MIT License
from 0xPrateek

def normalequation(x,y):

    xt = np.transpose(x)
    theta = np.linalg.inv(xt*x)*(np.transpose(x)*y)
    return theta

def cost_function(x,y,theta0,theta1):                   # This function is used for calculating Mean squared error or for minimization of cost function value.

3 Source : prep_noise.py
with MIT License
from a-n-rose

def wave2stft(np_array,sr):
    stft = librosa.stft(np_array,hop_length=int(0.01*sr),n_fft=int(0.025*sr))
    stft = np.transpose(stft)
    return stft
    
def get_energy(stft):

3 Source : prep_noise.py
with MIT License
from a-n-rose

def samps2stft(y, sr):
    if len(y)%2 != 0:
        y = y[:-1]
    #print("shape of samples: {}".format(y.shape))
    stft = librosa.stft(y)
    #print("shape of stft: {}".format(stft.shape))
    stft = np.transpose(stft)
    #print("transposed shape: {}".format(stft.shape))
    return stft


def stft2samps(stft,len_origsamp):

3 Source : prep_noise.py
with MIT License
from a-n-rose

def stft2samps(stft,len_origsamp):
    #print("shape of stft: {}".format(stft.shape))
    istft = np.transpose(stft.copy())
    ##print("transposed shape: {}".format(istft.shape))
    samples = librosa.istft(istft,length=len_origsamp)
    return samples

def stft2power(stft_matrix):

3 Source : rednoise_fun.py
with MIT License
from a-n-rose

def wave2stft(wavefile):
    y, sr = librosa.load(wavefile)
    if len(y)%2 != 0:
        y = y[:-1]
    stft = librosa.stft(y)
    stft = np.transpose(stft)
    return stft, y, sr

def stft2wave(stft,len_origsamp):

3 Source : rednoise_fun.py
with MIT License
from a-n-rose

def stft2wave(stft,len_origsamp):
    istft = np.transpose(stft.copy())
    samples = librosa.istft(istft,length=len_origsamp)
    return samples

def stft2power(stft_matrix):

3 Source : analyse_audio.py
with MIT License
from a-n-rose

def wave2stft(wavefile):
    y, sr = librosa.load(wavefile)
    if len(y)%2 != 0:
        y = y[:-1]
    stft = librosa.stft(y)
    stft = np.transpose(stft)
    return stft, y, sr

def wave2stft_long(wavefile):

3 Source : analyse_audio.py
with MIT License
from a-n-rose

def wave2stft_long(wavefile):
    y, sr = librosa.load(wavefile)
    if len(y)%2 != 0:
        y = y[:-1]
    stft = librosa.stft(y,hop_length=int(interval*sr),n_fft=int(0.256*sr))
    stft = np.transpose(stft)
    return stft, y, sr

def stft2wave(stft,len_origsamp):

3 Source : analyse_audio.py
with MIT License
from a-n-rose

def stft2wave(stft,len_origsamp):
    sr=22050
    istft = np.transpose(stft.copy())
    samples = librosa.istft(istft,length=len_origsamp)
    return samples

def stft2power(stft_matrix):

3 Source : compare_signals.py
with MIT License
from a-n-rose

def long_term_info(y,sr):
    interval = 0.01
    window = 0.256
    start_stft = time.time()
    stft = librosa.stft(y,hop_length=int(interval*sr),n_fft=int(window*sr))
    end_stft = time.time()
    stft = np.transpose(stft)
    power = np.abs(stft)**2
    start_pitch = time.time()
    pitch, mag = librosa.piptrack(y=y,sr=sr,hop_length=int(interval*sr),n_fft=int(window*sr))
    end_pitch = time.time()
    duration_stft = end_stft - start_stft
    duration_pitch = end_pitch - start_pitch
    pitch = np.transpose(pitch)
    return stft,power,pitch

def clip_around_sounds(sound_type,stft,samples_length,energy_length,start_index,end_index,sr):

3 Source : plot_double_func_with_theory.py
with MIT License
from a-norcliffe

def run_ode(x):
    z0 = [start[x], 0]
    z_arr = integrate.odeint(derivatives, z0, times)
    z_arr = np.transpose(z_arr)
    x_arr = z_arr[0]
    a_arr = z_arr[1]
    np.save(filename+names[x]+'_theory_x.npy', x_arr)
    np.save(filename+names[x]+'_theory_a.npy', a_arr)
  
run_ode(0)

3 Source : custom_transforms.py
with MIT License
from AaltoML

    def __call__(self, images, intrinsics):
        tensors = []
        for im in images:
            # put it from HWC to CHW format
            im = np.transpose(im, (2, 0, 1))
            # handle numpy array
            #tensors.append(torch.from_numpy(im).float()/255)
            tensors.append(torch.from_numpy(im).float())
        return tensors, intrinsics

3 Source : autoencoder_diabimmune.py
with MIT License
from aametwally

def import_data(InputFile):
	df = pd.read_csv(InputFile, sep=",", index_col=0)
	features = list(df.index)
	samples = list(df)
	data = np.transpose(df.as_matrix())
	return data, features, samples
	
	
def generate_metadata(samples, MetadataFile):

3 Source : util.py
with BSD 2-Clause "Simplified" License
from AAnoosheh

def tensor2im(image_tensor, imtype=np.uint8):
    image_numpy = image_tensor[0].cpu().float().numpy()
    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
    if image_numpy.shape[2]   <   3:
        image_numpy = np.dstack([image_numpy]*3)
    return image_numpy.astype(imtype)

def gkern_2d(size=5, sigma=3):

3 Source : model.py
with MIT License
from Abhishek-Doshi

def findCosineSimilarity(source_representation, test_representation):
    a = np.matmul(np.transpose(source_representation), test_representation)
    b = np.sum(np.multiply(source_representation, source_representation))
    c = np.sum(np.multiply(test_representation, test_representation))
    return (a / (np.sqrt(b) * np.sqrt(c)))

def findCosineDifference(source_representation, test_representation):

3 Source : model.py
with MIT License
from Abhishek-Doshi

def findCosineDifference(source_representation, test_representation):
    a = np.matmul(np.transpose(source_representation), test_representation)
    b = np.sum(np.multiply(source_representation, source_representation))
    c = np.sum(np.multiply(test_representation, test_representation))
    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
 
def findEuclideanDistance(source_representation, test_representation):

3 Source : train.py
with MIT License
from ace19-dev

def display_data(image):
    # display image to verify
    image = image.numpy()
    image = np.transpose(image, (0, 2, 3, 1))
    # # assets not np.any(np.isnan(image))
    n_batch = image.shape[0]
    # n_view = train_batch_xs.shape[1]
    for i in range(n_batch):
        img = image[i]
        # scipy.misc.toimage(img).show() Or
        img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)
        cv2.imwrite('/home/ace19/Pictures/' + str(i) + '.png', img)
        # cv2.imshow(str(train_batch_ys[idx]), img)
        cv2.waitKey(100)
        cv2.destroyAllWindows()


class AverageMeter(object):

3 Source : lmk2angle.py
with Apache License 2.0
from achao2013

def isRotationMatrix(R):
    ''' checks if a matrix is a valid rotation matrix(whether orthogonal or not)
    '''
    Rt = np.transpose(R)
    shouldBeIdentity = np.dot(Rt, R)
    I = np.identity(3, dtype = R.dtype)
    n = np.linalg.norm(I - shouldBeIdentity)
    return n   <   1e-6
def matrix2angle(R):

3 Source : camera.py
with Apache License 2.0
from achao2013

def isRotationMatrix(R):
    ''' checks if a matrix is a valid rotation matrix(whether orthogonal or not)
    '''
    Rt = np.transpose(R)
    shouldBeIdentity = np.dot(Rt, R)
    I = np.identity(3, dtype = R.dtype)
    n = np.linalg.norm(I - shouldBeIdentity)
    return n   <   1e-6

def matrix2angle(R):

3 Source : __init__.py
with Apache License 2.0
from achao2013

def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
    image_numpy = image_tensor[0].cpu().float().numpy()
    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
    return image_numpy.astype(imtype)

def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):

3 Source : __init__.py
with Apache License 2.0
from achao2013

def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
    image_numpy = image_tensor[0].cpu().float().numpy()
    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
    return image_numpy.astype(imtype)

def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):

3 Source : CycleGAN.py
with MIT License
from Adi-iitd

    def show_image(image):
        
        image = np.transpose((image + 1) / 2, (1, 2, 0))
        plt.imshow(image)
        
        
    @staticmethod

3 Source : CycleGAN.py
with MIT License
from Adi-iitd

    def tensor_to_numpy(tensor):
        
        tensor = (tensor.cpu().clone() + 1) / 2
        if   len(tensor.shape) == 3: tensor = np.transpose(tensor, (1, 2, 0))
        elif len(tensor.shape) == 4: tensor = np.transpose(tensor, (0, 2, 3, 1))
        
        return tensor

    
    @staticmethod

3 Source : train_SE.py
with MIT License
from adigasu

def preprocessData(imArray):

    # [w, h, b] --> [b, h, w, ch], ch =1
    imgWidth, imgHeight, nbatch = imArray.shape
    imArray = np.transpose(imArray, (2,1,0))
    imArray = np.reshape(imArray, (nbatch, imgHeight, imgWidth, 1))
    imArray = (imArray.astype('float32'))/ 255

    return imArray

# Center crop image
def crop_center(img, cX, cY):

3 Source : visualize.py
with MIT License
from aditya30394

def make_image(img, mean=(0,0,0), std=(1,1,1)):
    for i in range(0, 3):
        img[i] = img[i] * std[i] + mean[i]    # unnormalize
    npimg = img.numpy()
    return np.transpose(npimg, (1, 2, 0))

def gauss(x,a,b,c):

3 Source : test_numeric.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_transpose(self):
        arr = [[1, 2], [3, 4], [5, 6]]
        tgt = [[1, 3, 5], [2, 4, 6]]
        assert_equal(np.transpose(arr, (1, 0)), tgt)

    def test_var(self):

3 Source : test_regression.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_svd_build(self):
        # Ticket 627.
        a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
        m, n = a.shape
        u, s, vh = linalg.svd(a)

        b = dot(transpose(u[:, n:]), a)

        assert_array_almost_equal(b, np.zeros((2, 2)))

    def test_norm_vector_badarg(self):

3 Source : test_frame.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_numpy_transpose(self):
        sdf = SparseDataFrame([1, 2, 3], index=[1, 2, 3], columns=['a'])
        result = np.transpose(np.transpose(sdf))
        tm.assert_sp_frame_equal(result, sdf)

        msg = "the 'axes' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, np.transpose, sdf, axes=1)

    def test_combine_first(self):

3 Source : test_base.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_numpy_transpose(self):
        for obj in self.objs:
            if isinstance(obj, Index):
                tm.assert_index_equal(np.transpose(obj), obj)
            else:
                tm.assert_series_equal(np.transpose(obj), obj)

            tm.assert_raises_regex(ValueError, self.errmsg,
                                   np.transpose, obj, axes=1)


class TestNoNewAttributesMixin(object):

3 Source : test_mmio.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_random_symmetric_float(self):
        sz = (20, 20)
        a = np.random.random(sz)
        a = a + transpose(a)
        self.check(a, (20, 20, 400, 'array', 'real', 'symmetric'))

    def test_random_rectangular_float(self):

3 Source : test_mmio.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_random_symmetric_float(self):
        sz = (20, 20)
        a = np.random.random(sz)
        a = a + transpose(a)
        a = scipy.sparse.csr_matrix(a)
        self.check(a, (20, 20, 210, 'coordinate', 'real', 'symmetric'))

    def test_random_rectangular_float(self):

3 Source : test_basic.py
with GNU General Public License v3.0
from adityaprakash-bobby

def direct_lstsq(a, b, cmplx=0):
    at = transpose(a)
    if cmplx:
        at = conjugate(at)
    a1 = dot(at, a)
    b1 = dot(at, b)
    return solve(a1, b1)


class TestLstsq(object):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_complex_eig(self):
        a = [[1,2],[-2,1]]
        w,vl,vr = eig(a,left=1,right=1)
        assert_array_almost_equal(w, array([1+2j, 1-2j]))
        for i in range(2):
            assert_array_almost_equal(dot(a,vr[:,i]),w[i]*vr[:,i])
        for i in range(2):
            assert_array_almost_equal(dot(conjugate(transpose(a)),vl[:,i]),
                                      conjugate(w[i])*vl[:,i])

    def test_simple_complex(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_complex(self):
        a = [[1,2,3],[1,2,3],[2,5,6+1j]]
        w,vl,vr = eig(a,left=1,right=1)
        for i in range(3):
            assert_array_almost_equal(dot(a,vr[:,i]),w[i]*vr[:,i])
        for i in range(3):
            assert_array_almost_equal(dot(conjugate(transpose(a)),vl[:,i]),
                                      conjugate(w[i])*vl[:,i])

    def test_gh_3054(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_check_finite(self):
        a = [[1,2,3],[1,2,3],[2,5,6]]
        w,v = eig(a, check_finite=False)
        exact_w = [(9+sqrt(93))/2,0,(9-sqrt(93))/2]
        v0 = array([1,1,(1+sqrt(93)/3)/2])
        v1 = array([3.,0,-1])
        v2 = array([1,1,(1-sqrt(93)/3)/2])
        v0 = v0 / sqrt(dot(v0,transpose(v0)))
        v1 = v1 / sqrt(dot(v1,transpose(v1)))
        v2 = v2 / sqrt(dot(v2,transpose(v2)))
        assert_array_almost_equal(w,exact_w)
        assert_array_almost_equal(v0,v[:,0]*sign(v[0,0]))
        assert_array_almost_equal(v1,v[:,1]*sign(v[0,1]))
        assert_array_almost_equal(v2,v[:,2]*sign(v[0,2]))
        for i in range(3):
            assert_array_almost_equal(dot(a,v[:,i]),w[i]*v[:,i])

    def test_not_square_error(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple(self):
        a = [[1,2,3],[1,20,3],[2,5,6]]
        for full_matrices in (True, False):
            u,s,vh = svd(a, full_matrices=full_matrices,
                         lapack_driver=self.lapack_driver)
            assert_array_almost_equal(dot(transpose(u),u),identity(3))
            assert_array_almost_equal(dot(transpose(vh),vh),identity(3))
            sigma = zeros((u.shape[0],vh.shape[0]),s.dtype.char)
            for i in range(len(s)):
                sigma[i,i] = s[i]
            assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_simple_singular(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_singular(self):
        a = [[1,2,3],[1,2,3],[2,5,6]]
        for full_matrices in (True, False):
            u,s,vh = svd(a, full_matrices=full_matrices,
                         lapack_driver=self.lapack_driver)
            assert_array_almost_equal(dot(transpose(u),u),identity(3))
            assert_array_almost_equal(dot(transpose(vh),vh),identity(3))
            sigma = zeros((u.shape[0],vh.shape[0]),s.dtype.char)
            for i in range(len(s)):
                sigma[i,i] = s[i]
            assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_simple_underdet(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_underdet(self):
        a = [[1,2,3],[4,5,6]]
        for full_matrices in (True, False):
            u,s,vh = svd(a, full_matrices=full_matrices,
                         lapack_driver=self.lapack_driver)
            assert_array_almost_equal(dot(transpose(u),u),identity(u.shape[0]))
            sigma = zeros((u.shape[0],vh.shape[0]),s.dtype.char)
            for i in range(len(s)):
                sigma[i,i] = s[i]
            assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_simple_overdet(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_overdet(self):
        a = [[1,2],[4,5],[3,4]]
        for full_matrices in (True, False):
            u,s,vh = svd(a, full_matrices=full_matrices,
                         lapack_driver=self.lapack_driver)
            assert_array_almost_equal(dot(transpose(u),u), identity(u.shape[1]))
            assert_array_almost_equal(dot(transpose(vh),vh),identity(2))
            sigma = zeros((u.shape[1],vh.shape[0]),s.dtype.char)
            for i in range(len(s)):
                sigma[i,i] = s[i]
            assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_random(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_random(self):
        n = 20
        m = 15
        for i in range(3):
            for a in [random([n,m]),random([m,n])]:
                for full_matrices in (True, False):
                    u,s,vh = svd(a, full_matrices=full_matrices,
                                 lapack_driver=self.lapack_driver)
                    assert_array_almost_equal(dot(transpose(u),u),identity(u.shape[1]))
                    assert_array_almost_equal(dot(vh, transpose(vh)),identity(vh.shape[0]))
                    sigma = zeros((u.shape[1],vh.shape[0]),s.dtype.char)
                    for i in range(len(s)):
                        sigma[i,i] = s[i]
                    assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_simple_complex(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_complex(self):
        a = [[1,2,3],[1,2j,3],[2,5,6]]
        for full_matrices in (True, False):
            u,s,vh = svd(a, full_matrices=full_matrices,
                         lapack_driver=self.lapack_driver)
            assert_array_almost_equal(dot(conj(transpose(u)),u),identity(u.shape[1]))
            assert_array_almost_equal(dot(conj(transpose(vh)),vh),identity(vh.shape[0]))
            sigma = zeros((u.shape[0],vh.shape[0]),s.dtype.char)
            for i in range(len(s)):
                sigma[i,i] = s[i]
            assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_random_complex(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_random_complex(self):
        n = 20
        m = 15
        for i in range(3):
            for full_matrices in (True, False):
                for a in [random([n,m]),random([m,n])]:
                    a = a + 1j*random(list(a.shape))
                    u,s,vh = svd(a, full_matrices=full_matrices,
                                 lapack_driver=self.lapack_driver)
                    assert_array_almost_equal(dot(conj(transpose(u)),u),identity(u.shape[1]))
                    # This fails when [m,n]
                    # assert_array_almost_equal(dot(conj(transpose(vh)),vh),identity(len(vh),dtype=vh.dtype.char))
                    sigma = zeros((u.shape[1],vh.shape[0]),s.dtype.char)
                    for i in range(len(s)):
                        sigma[i,i] = s[i]
                    assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_crash_1580(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_check_finite(self):
        a = [[1,2,3],[1,20,3],[2,5,6]]
        u,s,vh = svd(a, check_finite=False, lapack_driver=self.lapack_driver)
        assert_array_almost_equal(dot(transpose(u),u),identity(3))
        assert_array_almost_equal(dot(transpose(vh),vh),identity(3))
        sigma = zeros((u.shape[0],vh.shape[0]),s.dtype.char)
        for i in range(len(s)):
            sigma[i,i] = s[i]
        assert_array_almost_equal(dot(dot(u,sigma),vh),a)

    def test_gh_5039(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple(self):
        a = [[8,2,3],[2,9,3],[5,3,6]]
        q,r = qr(a)
        assert_array_almost_equal(dot(transpose(q),q),identity(3))
        assert_array_almost_equal(dot(q,r),a)

    def test_simple_left(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_pivoting(self):
        a = np.asarray([[8,2,3],[2,9,3],[5,3,6]])
        q,r,p = qr(a, pivoting=True)
        d = abs(diag(r))
        assert_(all(d[1:]   <  = d[:-1]))
        assert_array_almost_equal(dot(transpose(q),q),identity(3))
        assert_array_almost_equal(dot(q,r),a[:,p])
        q2,r2 = qr(a[:,p])
        assert_array_almost_equal(q,q2)
        assert_array_almost_equal(r,r2)

    def test_simple_left_pivoting(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_trap(self):
        a = [[8,2,3],[2,9,3]]
        q,r = qr(a)
        assert_array_almost_equal(dot(transpose(q),q),identity(2))
        assert_array_almost_equal(dot(q,r),a)

    def test_simple_trap_pivoting(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_trap_pivoting(self):
        a = np.asarray([[8,2,3],[2,9,3]])
        q,r,p = qr(a, pivoting=True)
        d = abs(diag(r))
        assert_(all(d[1:]   <  = d[:-1]))
        assert_array_almost_equal(dot(transpose(q),q),identity(2))
        assert_array_almost_equal(dot(q,r),a[:,p])
        q2,r2 = qr(a[:,p])
        assert_array_almost_equal(q,q2)
        assert_array_almost_equal(r,r2)

    def test_simple_tall(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_tall(self):
        # full version
        a = [[8,2],[2,9],[5,3]]
        q,r = qr(a)
        assert_array_almost_equal(dot(transpose(q),q),identity(3))
        assert_array_almost_equal(dot(q,r),a)

    def test_simple_tall_pivoting(self):

3 Source : test_decomp.py
with GNU General Public License v3.0
from adityaprakash-bobby

    def test_simple_tall_pivoting(self):
        # full version pivoting
        a = np.asarray([[8,2],[2,9],[5,3]])
        q,r,p = qr(a, pivoting=True)
        d = abs(diag(r))
        assert_(all(d[1:]   <  = d[:-1]))
        assert_array_almost_equal(dot(transpose(q),q),identity(3))
        assert_array_almost_equal(dot(q,r),a[:,p])
        q2,r2 = qr(a[:,p])
        assert_array_almost_equal(q,q2)
        assert_array_almost_equal(r,r2)

    def test_simple_tall_e(self):

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