LPC_stats_2017/HW3/p4.py

#!/usr/bin/env python3
""" LPC stats HW2, Problem 4
    Author: Caleb Fangmeier
    Created: Oct. 8, 2017
"""
from scipy.stats import norm, uniform, cauchy, mode
from numpy import logspace, zeros, max, min, mean, median, var
import matplotlib.pyplot as plt


distributions = {
    'uniform': lambda a, N: uniform.rvs(loc=a-0.5, scale=1, size=N),
    'gaussian': lambda a, N: norm.rvs(loc=a, scale=1, size=N),
    'cauchy': lambda a, N: cauchy.rvs(loc=a, size=N)
}

estimators = {
    'midrange': lambda xs: max(xs) - min(xs),
    'mean': lambda xs: mean(xs),
    'median': lambda xs: median(xs),
    'mode': lambda xs: mode(xs).mode[0]
}


def var_of_est(dist_name, est_name, N, a=1):
    M = 500
    estimates = zeros(M)
    for i in range(M):  # run M experiments to estimate variance
        data = distributions[dist_name](a, N)
        estimates[i] = estimators[est_name](data)
    return var(estimates)


plt.figure()
for i, distribution in enumerate(distributions):
    plt.subplot(2, 2, i+1)
    for estimator in estimators:
        Ns = logspace(1, 3, 30, dtype=int)
        vars = zeros(30)
        for i, N in enumerate(Ns):
            vars[i] = var_of_est(distribution, estimator, N)
        plt.plot(Ns, vars, label=estimator)
    plt.title(distribution)
    plt.xlabel('N')
    plt.ylabel(r'$\sigma^2$')
    plt.xscale('log')
    plt.yscale('log')
    plt.tight_layout()
    plt.legend()

plt.show()