pca_normalize_dataset.py File Reference

Namespaces
namespace	python
namespace	python.utils
namespace	python.utils.pca_normalize_dataset

Functions
	python.utils.pca_normalize_dataset.clean_data (data)
	leave only the data points with coordinated greater than zero is convinient for some problems, i.e.
	python.utils.pca_normalize_dataset.create_logger ()
	python.utils.pca_normalize_dataset.norm (mat)
	normalization on [0,1] interval
	python.utils.pca_normalize_dataset.remove_outliers (mat, koef, target=None)
	remove outliers, where the points deviate on more than koef times standard deviation from the mean

Variables
	python.utils.pca_normalize_dataset.action
	python.utils.pca_normalize_dataset.args
	python.utils.pca_normalize_dataset.C = cov(interest_data)
	python.utils.pca_normalize_dataset.data = genfromtxt(options.csv_dir + filename, skiprows=1, delimiter=',')
	python.utils.pca_normalize_dataset.default
	python.utils.pca_normalize_dataset.delimiter
	python.utils.pca_normalize_dataset.dest
	python.utils.pca_normalize_dataset.filename = options.file_in
	python.utils.pca_normalize_dataset.help
	python.utils.pca_normalize_dataset.interest_data = data - means
	python.utils.pca_normalize_dataset.interest_data_transformed = dot(invV, interest_data).T
	python.utils.pca_normalize_dataset.invV = inv(V)
	python.utils.pca_normalize_dataset.logger = create_logger()
	python.utils.pca_normalize_dataset.means = mean(data,axis=0)
	python.utils.pca_normalize_dataset.options
	python.utils.pca_normalize_dataset.parser = optparse.OptionParser()
	python.utils.pca_normalize_dataset.target = data[:, options.target_column]
	python.utils.pca_normalize_dataset.type
	python.utils.pca_normalize_dataset.u
	python.utils.pca_normalize_dataset.V