tansey

'''Fast sampling from a multivariate normal with covariance or precision
    parameterization. Supports sparse arrays. Params:
        - mu: If provided, assumes the model is N(mu, Q)
        - mu_part: If provided, assumes the model is N(Q mu_part, Q).
                    This is common in many conjugate Gibbs steps.
        - sparse: If true, assumes we are working with a sparse Q
        - precision: If true, assumes Q is a precision matrix (inverse covariance)
        - chol_factor: If true, assumes Q is a (lower triangular) Cholesky
                        decomposition of the covariance matrix
                        (or of the precision matrix if precision=True).

tansey

import numpy as np
import numpy.ma as ma


def cap_outliers(points, thresh=3.5, data=None, median=None, med_abs_deviation=None):
    '''
    Cap outliers to be within a certain number of median deviations.
    '''
    if type(points) is np.float64:
        points = np.array([points])

tansey

        public static double SampleGaussian(Random random, double mean, double stddev)
        {
            // The method requires sampling from a uniform random of (0,1]
            // but Random.NextDouble() returns a sample of [0,1).
            double x1 = 1 - random.NextDouble();
            double x2 = 1 - random.NextDouble();

            double y1 = Math.Sqrt(-2.0 * Math.Log(x1)) * Math.Cos(2.0 * Math.PI * x2);
            return y1 * stddev + mean;
        }

tansey

        /// <summary>
        /// Fits a line to a collection of (x,y) points.
        /// </summary>
        /// <param name="xVals">The x-axis values.</param>
        /// <param name="yVals">The y-axis values.</param>
        /// <param name="inclusiveStart">The inclusive inclusiveStart index.</param>
        /// <param name="exclusiveEnd">The exclusive exclusiveEnd index.</param>
        /// <param name="rsquared">The r^2 value of the line.</param>
        /// <param name="yintercept">The y-intercept value of the line (i.e. y = ax + b, yintercept is b).</param>
        /// <param name="slope">The slop of the line (i.e. y = ax + b, slope is a).</param>