https://github.com/tensorly/tensorly
Revision 8a35614d386b0082a95238e147245754d050eea2 authored by Jean Kossaifi on 14 December 2017, 16:04:07 UTC, committed by Jean Kossaifi on 14 December 2017, 16:04:07 UTC
1 parent 2214d9d
Tip revision: 8a35614d386b0082a95238e147245754d050eea2 authored by Jean Kossaifi on 14 December 2017, 16:04:07 UTC
Tensor regression on any context
Tensor regression on any context
Tip revision: 8a35614
synthetic.py
import numpy as np
from ..random import check_random_state
from .. import backend as T
def gen_image(region='swiss', image_height=20, image_width=20,
n_channels=None, weight_value=1):
"""Generates an image for regression testing
Parameters
----------
region : {'swiss', 'rectangle'}
image_height : int, optional
image_width : int, optional
weight_value : float, optional
n_channels : int or None, optional
if not None, the resulting image will have a third dimension
Returns
-------
ndarray
array of shape ``(image_height, image_width)``
or ``(image_height, image_width, n_channels)``
array for which all values are zero except the region specified
"""
weight = np.zeros((image_height, image_width), dtype=np.float)
if region is "swiss":
slim_width = (image_width // 2) - (image_width // 10 + 1)
large_width = (image_width // 2) - (image_width // 3 + 1)
slim_height = (image_height // 2) - (image_height // 10 + 1)
large_height = (image_height // 2) - (image_height // 3 + 1)
weight[large_height:-large_height, slim_width:-slim_width] = weight_value
weight[slim_height:-slim_height, large_width:-large_width] = weight_value
elif region is "rectangle":
large_height = (image_height // 2) - (image_height // 4)
large_width = (image_width // 2) - (image_width // 4)
weight[large_height:-large_height, large_width:-large_width] = weight_value
elif region is "circle":
radius = image_width // 3
cy = image_width // 2
cx = image_height // 2
y, x = np.ogrid[-radius: radius, -radius: radius]
index = x**2 + y**2 <= radius**2
weight[cy-radius:cy+radius, cx-radius:cx+radius][index] = 1
if n_channels is not None and weight.ndim == 2:
weight = np.concatenate([weight[..., None]]*n_channels, axis=-1)
return T.tensor(weight)
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