MaxBareiss

# Euclidean distance.
def euc_dist(pt1,pt2):
    return math.sqrt((pt2[0]-pt1[0])*(pt2[0]-pt1[0])+(pt2[1]-pt1[1])*(pt2[1]-pt1[1]))

def _c(ca,i,j,P,Q):
    if ca[i,j] > -1:
        return ca[i,j]
    elif i == 0 and j == 0:
        ca[i,j] = euc_dist(P[0],Q[0])
    elif i > 0 and j == 0:

aparrish

jtrive84

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def modelcomp(df, outpath, titlestr=None):
    """
    Generate partial residual model comparison exhibits.
    """
    try:
        pdf = PdfPages(outpath)
        df = df.rename({"INCIDENCE_MNTH":"actual_count"}, axis=1)
        keepfields = [i for i in df.columns if i.endswith("count")] + ["EXPOSURE"]
        varcols = [i for i in df.columns if i not in keepfields]
        cfields = [i for i in df.columns if i.endswith("count")]

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import datetime
import itertools
import os
import os.path
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from matplotlib.backends.backend_pdf import PdfPages

jtrive84

Estimating Logistic Regression Coefficents From Scratch in Python

In this post, we'll demonstrate how to estimate the coefficents of a Logistic Regression model using the Fisher Scoring algorithm in Python. We will then compare our estimates to those generated by scikit-learn’s linear_model.LogisticRegression class when fed the same dataset.

In a Generalized Linear Model, the response may have any distribution from the exponential family, and rather than assuming the mean is a linear function of the explnatory variables, we assume that a function of the mean, or the link function, is a linear function of the