| #!/usr/bin/python2.7 |
| # Copyright (c) 2014 The Chromium OS Authors. All rights reserved. |
| # Use of this source code is governed by a BSD-style license that can be |
| # found in the LICENSE file. |
| |
| import numpy |
| |
| |
| def LinearRegression(x, y): |
| """Perform a linear regression using numpy. |
| |
| @param x: Array of x-coordinates of the samples |
| @param y: Array of y-coordinates of the samples |
| @return: ((slope, intercept), r-squared) |
| """ |
| # p(x) = p[0]*x**1 + p[1] |
| p, (residual,) = numpy.polyfit(x, y, 1, full=True)[:2] |
| # Calculate the coefficient of determination (R-squared) from the |
| # "residual sum of squares" |
| # Reference: |
| # http://en.wikipedia.org/wiki/Coefficient_of_determination |
| r2 = 1 - (residual / (y.size*y.var())) |
| |
| # Alternate calculation for R-squared: |
| # |
| # Calculate the coefficient of determination (R-squared) as the |
| # square of the sample correlation coefficient, |
| # which can be calculated from the variances and covariances. |
| # Reference: |
| # http://en.wikipedia.org/wiki/Correlation#Pearson.27s_product-moment_coefficient |
| #V = numpy.cov(x, y, ddof=0) |
| #r2 = (V[0,1]*V[1,0]) / (V[0,0]*V[1,1]) |
| |
| return p, r2 |
| |
| |
| def FactsToNumpyArray(facts, dtype): |
| """Convert "facts" (list of dicts) to a numpy array. |
| |
| @param facts: A list of dicts. Each dict must have keys matching the field |
| names in dtype. |
| @param dtype: A numpy.dtype used to fill the array from facts. The dtype |
| must be a "structured array". ie: |
| numpy.dtype([('loops', numpy.int), ('cycles', numpy.int)]) |
| @returns: A numpy.ndarray with dtype=dtype filled with facts. |
| """ |
| a = numpy.zeros(len(facts), dtype=dtype) |
| for i, f in enumerate(facts): |
| a[i] = tuple(f[n] for n in dtype.names) |
| return a |