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generate toy datasets based on code by Mark Rogers
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| Function Details |
Generates a 2-D noisy sine wave
Parameters:
xlim - list of length 2 that delimits the x value range
ylim - list of length 2 that delimits the y value range
n - number of data points
Note: for use with PyML demo2d, only use x and y values
between -1 and 1
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a wrapper around numpy's random.multivariate_normal function
Generates data from a Gaussian distribution with mean mu
and standard deviation sigma
Parameters:
mu - mean
sigma - variance (either a float, list or square matrix)
n - number of points to generate
Note: for use with PyML demo2d, only use mu1 and mu2
values that keep populations between -1 and 1
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Creates two populations, usually linearly-separable, but with vastly different variance. Simulates a problem where one population has significantly more noise than another. Data are output in a CSV format suitable for creating a PyML VectorDataSet (labelsColumn=1). |
Uses sine-wave populations to create two class populations that meander close to each other. Data are output in a CSV format suitable for creating a PyML VectorDataSet (labelsColumn=1). |
Creates two linearly-separable populations, one centered at (-.5,0) and the other at (0.5,0). Data are output in a CSV format suitable for creating a PyML VectorDataSet (labelsColumn=1). |
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USAGE
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| Generated by Epydoc 3.0.1 on Fri May 2 12:39:26 2008 | http://epydoc.sourceforge.net |