smoothingKernelPlot.ipynb

Kernel: Unknown Kernel

import numpy as np
import matplotlib.pyplot as plt


def plotColors():

    colors = ["b", "r", "k", "g", "c", "y", "m", "r", "b", "k", "g", "c", "y", "m"]
    symbols = ["o", "x", "*", ">", "<", "^", "v", "+", "p", "h", "s", "d", "o", "x"]
    styles = ["-", ":", "-.", "--", "-", ":", "-.", "--", "-", ":", "-.", "--", "-", ":", "-.", "--"]
    Str = []
    for i in range(0, len(colors)):
        Str.append(colors[i] + styles[i])
    return [styles, colors, symbols, Str]


def box(u):
    return (1 / 2) * (abs(u) <= 1)


def epa(u):
    return (3 / 4) * (1 - np.power(u, 2)) * (abs(u) <= 1)


def tri(u):
    return (70 / 81) * np.power((1 - np.power(abs(u), 3)), 3) * (abs(u) <= 1)


def gauss(u):
    return (1 / np.sqrt(2 * np.pi)) * np.exp(-np.power(u, 2) / 2)


fns = [box, epa, tri, gauss]
names = ["Boxcar", "Epanechnikov", "Tricube", "Gaussian"]

xs = np.arange(-1.5, 1.501, 0.01)
[styles, colors, symbols, Str] = plotColors()

smoothingKernalPlot = plt.figure()

for i in range(0, len(fns)):
    f = fns[i]
    fx = f(xs)
    b = xs[1] - xs[0]
    print("integral is " + str(sum(fx)))
    smoothingKernalPlot = plt.plot(xs, fx, styles[i] + colors[i])

smoothingKernalPlot = plt.legend(names)
# plt.savefig('smoothingKernelPlot')
plt.show()