MyStats.py

import math
import statistics as st
import numpy as np
from numpy.linalg import inv

def binomial(n, x, p):
  q = 1.0 - p
  return ((math.factorial(n) / (math.factorial(n - x) * math.factorial(x)))
      * math.pow(p, x) * math.pow(q, n - x))

def nbinomial(n, x, p):
  q = 1.0 - p
  return ((math.factorial(n - 1) / (math.factorial(n - x) * math.factorial(x - 1)))
      * math.pow(p, x) * math.pow(q, n - x))

def geomdist(n, p):
  q = 1.0 - p
  return p * math.pow(q, n - 1)

def poisson(x, l):
  return (math.pow(l, x) * math.exp(-l)) / math.factorial(x)

def normalAcc(mean, sd, x):
  return (1.0 + math.erf((x - mean) / (sd * math.sqrt(2)))) / 2


def pearson(dsa, dsb):
  ma = st.mean(dsa)
  mb = st.mean(dsb)
  sda = st.pstdev(dsa)
  sdb = st.pstdev(dsb)
  out = 0.0
  for i in range(0, len(dsa)):
    out += (dsa[i] - ma) * (dsb[i] - mb)
  out /= (len(dsa) * sda * sdb)
  return out

def lsregresion(da, db):
  m = pearson(da, db) * st.pstdev(db) / st.pstdev(da)
  b = st.mean(db) - m * st.mean(da)
  return (m, b)

def mlreg(x, y):        # X and Y matrices
  xtr  = x.transpose()  # X-transposed
  xint = xtr.dot(x)     # X-intermediate (for lack of a better name)
  xinv = inv(xint)      # X-inverse for inverse of X-intermediate
  preb = xinv.dot(xtr)  # pre-B
  b = preb.dot(y)       # matrix B, the answer
  return b              # Answer answered!

def mlrpredict(x, b):
  answer = x.dot(b)
  return answer[0][0]