CP tensor regression

Example on how to use `tensorly.regression.cp_regression.CPRegressor` to perform tensor regression.

```import matplotlib.pyplot as plt
from tensorly.base import tensor_to_vec, partial_tensor_to_vec
from tensorly.datasets.synthetic import gen_image
from tensorly.regression.cp_regression import CPRegressor
import tensorly as tl

# Parameter of the experiment
image_height = 25
image_width = 25
# shape of the images
patterns = ['rectangle', 'swiss', 'circle']
# ranks to test
ranks = [1, 2, 3, 4, 5]

# Generate random samples
rng = tl.check_random_state(1)
X = tl.tensor(rng.normal(size=(1000, image_height, image_width), loc=0, scale=1))

# 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):

# 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, n_iter_max=100, reg_W=1, verbose=0)

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

ax = fig.add_subplot(n_rows, n_columns, i*n_columns + j + 2)
ax.imshow(tl.to_numpy(estimator.weight_tensor_), cmap=plt.cm.OrRd, interpolation='nearest')
ax.set_axis_off()

if i == 0:
ax.set_title('Learned\nrank = {}'.format(rank))

plt.suptitle("CP tensor regression")
plt.show()
```

Total running time of the script: (0 minutes 5.451 seconds)

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