SG++-Doxygen-Documentation
python.utils.pca_normalize_dataset Namespace Reference

Functions

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

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

def 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

def python.utils.pca_normalize_dataset.create_logger ( )
def python.utils.pca_normalize_dataset.norm (   mat)

normalization on [0,1] interval

Parameters
matmatrix points row-wise
def 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
matmatrix points row-wise
koefkoefficient to determine the outliers
targetlist with target values for the points

References python.datasetAnalysis.mean.

Variable Documentation

python.utils.pca_normalize_dataset.action
python.utils.pca_normalize_dataset.args
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