python.utils.pca_normalize_dataset Namespace Reference

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

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

Function Documentation

clean_data()

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.

photometric redshift

create_logger()

python.utils.pca_normalize_dataset.create_logger

(

)

norm()

python.utils.pca_normalize_dataset.norm

(

mat

)

normalization on [0,1] interval

Parameters

mat	matrix points row-wise

remove_outliers()

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

Parameters

mat	matrix points row-wise
koef	koefficient to determine the outliers
target	list with target values for the points

Variable Documentation

action

python.utils.pca_normalize_dataset.action

args

python.utils.pca_normalize_dataset.args

C

python.utils.pca_normalize_dataset.C = cov(interest_data)

data

python.utils.pca_normalize_dataset.data = genfromtxt(options.csv_dir + filename, skiprows=1, delimiter=',')

default

python.utils.pca_normalize_dataset.default

delimiter

python.utils.pca_normalize_dataset.delimiter

dest

python.utils.pca_normalize_dataset.dest

filename

python.utils.pca_normalize_dataset.filename = options.file_in

help

python.utils.pca_normalize_dataset.help

interest_data

python.utils.pca_normalize_dataset.interest_data = data - means

interest_data_transformed

python.utils.pca_normalize_dataset.interest_data_transformed = dot(invV, interest_data).T

invV

python.utils.pca_normalize_dataset.invV = inv(V)

logger

python.utils.pca_normalize_dataset.logger = create_logger()

means

python.utils.pca_normalize_dataset.means = mean(data,axis=0)

options

python.utils.pca_normalize_dataset.options

parser

python.utils.pca_normalize_dataset.parser = optparse.OptionParser()

target

python.utils.pca_normalize_dataset.target = data[:, options.target_column]

type

python.utils.pca_normalize_dataset.type

u

python.utils.pca_normalize_dataset.u

V

python.utils.pca_normalize_dataset.V