Package pylearn :: Package datasets :: Module make_test_datasets
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Module make_test_datasets

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Functions [hide private]
 
make_triangles_rectangles_online_dataset(image_size=(10,10))
Make a binary classification dataset to discriminate triangle images from rectangle images.
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make_triangles_rectangles_dataset(n_examples=600, image_size=(10,10), cache=True)
Make a binary classification dataset to discriminate triangle images from rectangle images.
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make_triangles_rectangles_datasets(n_examples=600, train_frac=0.5, image_size=(10,10), cache=True)
Make two binary classification datasets to discriminate triangle images from rectangle images.
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make_artificial_datasets_from_function(n_inputs=1, n_targets=1, n_examples=20, train_frac=0.5, noise_level=0.1, params_shape=None, f=None, otherargs=None, b=None)
Make regression data of the form Y | X ~ Normal(f(X,theta,otherargs),noise_level^2) If n_inputs==1 then X is chosen at regular locations on the [-1,1] interval.
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Imports: ArrayDataSet, linear_predictor, kernel_predictor, add_newdocs


Function Details [hide private]

make_triangles_rectangles_datasets(n_examples=600, train_frac=0.5, image_size=(10,10), cache=True)

source code 

Make two binary classification datasets to discriminate triangle images from rectangle images. The first one is the training set, the second is the test set.

make_artificial_datasets_from_function(n_inputs=1, n_targets=1, n_examples=20, train_frac=0.5, noise_level=0.1, params_shape=None, f=None, otherargs=None, b=None)

source code 

Make regression data of the form
  Y | X ~ Normal(f(X,theta,otherargs),noise_level^2)
If n_inputs==1 then X is chosen at regular locations on the [-1,1] interval.
Otherwise X is sampled according to a Normal(0,1) on all dimensions (independently).
The parameters theta is a matrix of shape params_shape that is sampled from Normal(0,1).
Optionally theta[0] is set to the argument 'b', if b is provided.

Return a training set and a test set, by splitting the generated n_examples
according to the 'train_frac'tion.