| """This module contains one line functions that should, by all rights, be in numpy. | |
| """ | |
| import numpy as np | |
| ## Demean -- remove the mean from each column | |
| demean = lambda v: v-v.mean(0) | |
| demean.__doc__ = """Removes the mean from each column of [v].""" | |
| dm = demean | |
| ## Z-score -- z-score each column | |
| def zscore(v): | |
| s = v.std(0) | |
| m = v - v.mean(0) | |
| for i in range(len(s)): | |
| if s[i] != 0.: | |
| m[:, i] /= s[i] | |
| return m | |
| # zscore = lambda v: (v-v.mean(0))/v.std(0) | |
| zscore.__doc__ = """Z-scores (standardizes) each column of [v].""" | |
| zs = zscore | |
| ## Rescale -- make each column have unit variance | |
| rescale = lambda v: v/v.std(0) | |
| rescale.__doc__ = """Rescales each column of [v] to have unit variance.""" | |
| rs = rescale | |
| ## Matrix corr -- find correlation between each column of c1 and the corresponding column of c2 | |
| mcorr = lambda c1,c2: (zs(c1)*zs(c2)).mean(0) | |
| mcorr.__doc__ = """Matrix correlation. Find the correlation between each column of [c1] and the corresponding column of [c2].""" | |
| ## Cross corr -- find corr. between each row of c1 and EACH row of c2 | |
| xcorr = lambda c1,c2: np.dot(zs(c1.T).T,zs(c2.T)) / (c1.shape[1]) | |
| xcorr.__doc__ = """Cross-column correlation. Finds the correlation between each row of [c1] and each row of [c2].""" | |