This has spatial pooler code
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spatialpooler
LICENSE
README.md
patches10k-64img_binary.pickle
patches10k-64img_grayscale.pickle
patches2k-8img_binary.pickle
patches2k-8img_grayscale.pickle
patches_binary.pickle
patches_full-8img_binary.pickle
patches_full-8img_grayscale.pickle
patches_grayscale.pickle

README.md

SpatialPooler

This has spatial pooler code

Structure:

The spatialpooler package contains all the code.

The spatialpooler/BSP.py file contains the Binary Spatial Pooler implementation.

The spatialpooler/ASP.py file contains the Augmented Spatial Pooler implementation.

The spatialpooler/common.py file contains all functions common to both the BSP and ASP.

The spatialpooler/utils.py file contains all ancillary functions.

The spatialpooler/evaluate_experiments.py file contains the code use for the pooler's evaluation.

The spatialpooler/test package contains all test files.

Finally, the images directory contains the images that where used for training.

Prerequisites:

The BSP and ASP implementations depend on numpy, numexpr and matplotlib. The three can be installed using the Anaconda distribution by:

  • Downloading the installer appropriate for your OS from the Continuum Analytics' website.

  • Installing Anaconda.

  • Opening a terminal and typing:

      conda install numpy numexpr matplotlib
    

Use:

The BSP accepts boolean numpy arrays only. The ASP handles single channel (grayscale) images.

To perform the experiments the RGB images where transformed to grayscale and, in the case of the BSP, to boolean by setting the pixels with luminosity above the mean to 1 and below the mean to 0.

Binary Spatial Pooler:

In order to run the BSP, you can use the following command (from the repository's root):

spatialpooler/BSP.py --output_file ./columns2k-8img_bsp.pickle --patches_file patches2k-8img_binary.pickle

columns2k-8img_bsp.pickle is the name of the file where (pickled) trained columns, in the form of a 4D numpy array, will be stored.

patches2k-8img_binary.pickle is a pre-generated patches taken from some of the images in the images directory. It is a pickled numpy array of shape (2000, 16, 16) and type numpy.bool, generated using 8 images.

Augmented Spatial Pooler:

In order to run the ASP, you can use the following command (from the repository's root):

spatialpooler/ASP.py --output_file ./columns2k-8img_asp.pickle --patches_file patches2k-8img_grayscale.pickle

columns2k-8img_asp.pickle is the name of the file where (pickled) trained columns, in the form of a 4D numpy array, will be stored. patches2k-8img_grayscale.pickle is a pre-generated patches taken from some of the images in the images directory. It is a pickled numpy array of shape (2000, 16, 16) and type numpy.int, generated using 8 images.

Evaluation:

To build the reconstruction of an image set, and print some statistics about the poolers, run:

spatialpooler/evaluate_experiments.py --asp_columns_file columns10k-64img_asp.pickle --bsp_columns_file columns10k-64img_bsp.pickle --imgs_dir images --asp_out_dir rec_asp --bsp_out_dir rec_bsp

You can specify the --asp_columns_file and --bsp_columns_file together in the same run of evaluate_experiments.py, or separately in different runs; you must provide, however, at least one of them. These parameters' values should be the same as the value of the --output_file parameter used when training the ASP and BSP, respectively. In other words, they are numpy array representing the result of the training procedure, stored in pickled format.

The --imgs_dir parameter indicates the directory where the images used for evaluating the algorithms are stored.

Finally, the --asp_out_dir and --bsp_out_dir indicate the directory(ies) use to store the reconstructed images from the ASP and BSP algorithms, respectively. These arguments are not mandatory.