Python tensor_to_vec Examples

Example #1

File: plot_tucker_regression.py Project: zhangyaqian0701/tensorly

# Parameters of the plot, deduced from the data
n_rows = len(patterns)
n_columns = len(ranks) + 1
# Plot the three images
fig = plt.figure()

for i, pattern in enumerate(patterns):

    print('fitting pattern n.{}'.format(i))

    # Generate the original image
    weight_img = gen_image(region=pattern, image_height=image_height, image_width=image_width)
    weight_img = tl.tensor(weight_img)

    # Generate the labels
    y = tl.dot(partial_tensor_to_vec(X, skip_begin=1), tensor_to_vec(weight_img))

    # Plot the original weights
    ax = fig.add_subplot(n_rows, n_columns, i*n_columns + 1)
    ax.imshow(tl.to_numpy(weight_img), cmap=plt.cm.OrRd, interpolation='nearest')
    ax.set_axis_off()
    if i == 0:
        ax.set_title('Original\nweights')

    for j, rank in enumerate(ranks):
        print('fitting for rank = {}'.format(rank))

        # Create a tensor Regressor estimator
        estimator = TuckerRegressor(weight_ranks=[rank, rank], tol=10e-7, n_iter_max=100, reg_W=1, verbose=0)

        # Fit the estimator to the data

Example #2

File: plot_cp_regression.py Project: premjithb/tensorly

n_rows = len(patterns)
n_columns = len(ranks) + 1
# Plot the three images
fig = plt.figure()

for i, pattern in enumerate(patterns):

    # Generate the original image
    weight_img = gen_image(region=pattern,
                           image_height=image_height,
                           image_width=image_width)
    weight_img = tl.tensor(weight_img)

    # Generate the labels
    y = tl.dot(partial_tensor_to_vec(X, skip_begin=1),
               tensor_to_vec(weight_img))

    # Plot the original weights
    ax = fig.add_subplot(n_rows, n_columns, i * n_columns + 1)
    ax.imshow(tl.to_numpy(weight_img),
              cmap=plt.cm.OrRd,
              interpolation='nearest')
    ax.set_axis_off()
    if i == 0:
        ax.set_title('Original\nweights')

    for j, rank in enumerate(ranks):

        # Create a tensor Regressor estimator
        estimator = CPRegressor(weight_rank=rank,
                                tol=10e-7,

Example #3

File: tensor_decomp.py Project: mullachv/FreqMarching

def tensor_frob_norm(A):
    return np.sqrt(np.sum(np.square(tensor_to_vec(A))))

Example #4

File: plot_kruskal_regression.py Project: therealsheffield/tensorly

X = rng.normal(size=(1000, image_height, image_width), loc=0, scale=1)


# Paramters of the plot, deduced from the data
n_rows = len(patterns)
n_columns = len(ranks) + 1
# Plot the three images
fig = plt.figure()

for i, pattern in enumerate(patterns):

    # Generate the original image
    weight_img = gen_image(region=pattern, image_height=image_height, image_width=image_width)

    # Generate the labels
    y = partial_tensor_to_vec(X, skip_begin=1).dot(tensor_to_vec(weight_img))

    # Plot the original weights
    ax = fig.add_subplot(n_rows, n_columns, i*n_columns + 1)
    ax.imshow(weight_img, cmap=plt.cm.OrRd, interpolation='nearest')
    ax.set_axis_off()
    if i == 0:
        ax.set_title('Original\nweights')

    for j, rank in enumerate(ranks):

        # Create a tensor Regressor estimator
        estimator = KruskalRegressor(weight_rank=rank, tol=10e-7, n_iter_max=100, reg_W=1, verbose=0)

        # Fit the estimator to the data
        estimator.fit(X, y)

Example #5

File: tensor_decomp.py Project: mullachv/FreqMarching

def tensor_distance(A, B):
    return np.sum(np.square(tensor_to_vec(A) - tensor_to_vec(B))
                  ) / tensor_frob_norm(A) / tensor_frob_norm(B)