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/// <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> |
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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; | |
} |
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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]) |
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'''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). |
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import numpy as np | |
def pav(y): | |
""" | |
PAV uses the pair adjacent violators method to produce a monotonic | |
smoothing of y | |
translated from matlab by Sean Collins (2006) as part of the EMAP toolbox | |
Author : Alexandre Gramfort | |
license : BSD |
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import matplotlib.pyplot as plt | |
import numpy as np | |
import statsmodels.api as sm | |
def generalized_liang_sim_xy(N=500, P=500, S=100): | |
'''Generates data from a simple linear model''' | |
X = (np.random.normal(size=(N,1)) + np.random.normal(size=(N,P))) / 2. | |
w0 = np.random.normal(1, size=S//4) | |
w1 = np.random.normal(2, size=S//4) |
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''' | |
Program to generate valid time allocations of a mental ward staff. | |
Given: | |
Two staff lists. Each list applies for a specific window of time. Lists may | |
contain non-empty intersections of employees. | |
Each employee has a designation as RMN or HCA. |
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import numpy as np | |
from functools import partial | |
from scipy.optimize import fmin_l_bfgs_b | |
from sklearn.linear_model import LogisticRegression | |
def binary_mf(Y, nembeds, lam=None, lams=30, cv=5, max_steps=30, tol=1e-4, verbose=False): | |
# Convert to a log-space grid | |
if lam is None and isinstance(lams, int): | |
lams = np.exp(np.linspace(np.log(1e-2), np.log(1), lams)) |
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''' | |
A O(nlogn) time implementation of the knockoff filter. | |
Author: Wesley Tansey | |
Date: 3/27/2020 | |
''' | |
import numpy as np | |
def knockoff_filter(knockoff_stats, alpha, offset=1.0, is_sorted=False): | |
'''Perform the knockoffs selection procedure at the target FDR threshold. |
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''' | |
Heterogeneous factor modeling. | |
This model fits a heterogeneous factor model where columns may be: | |
1) Binary | |
2) Categorical | |
3) Gaussian | |
Everything is fit via alternating minimization and stochastic gradient descent. | |
The code relies on pytorch for SGD and a demo is included. |
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