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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 |
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 |
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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