4.KNN_Prediction_Grid.py

import numpy as np
import random
import scipy.stats as ss
import matplotlib.pyplot as plt

def make_prediction_grid(predictors,outcomes,limits,h,k):
    (x_min,x_max, y_min,y_max) = limits
    xs=np.arange(x_min,x_max,h)
    ys = np.arange(y_min,y_max, h)
    xx, yy = np.meshgrid(xs,ys)

    prediction_grid = np.zeros(xx.shape, dtype = int)
    for i,x in enumerate(xs):
        for j,y in enumerate(ys):
            p=np.array([x,y])
            prediction_grid[j,i] = knn_predict(p,predictors,outcomes,k)
    return (xx,yy,prediction_grid)

def plot_prediction_grid (xx, yy, prediction_grid, filename):
    """ Plot KNN predictions for every point on the grid."""
    from matplotlib.colors import ListedColormap
    background_colormap = ListedColormap (["hotpink","lightskyblue", "yellowgreen"])
    observation_colormap = ListedColormap (["red","blue","green"])
    plt.figure(figsize =(10,10))
    plt.pcolormesh(xx, yy, prediction_grid, cmap = background_colormap, alpha = 0.5)
    plt.scatter(predictors[:,0], predictors [:,1], c = outcomes, cmap = observation_colormap, s = 50)
    plt.xlabel('Variable 1'); plt.ylabel('Variable 2')
    plt.xticks(()); plt.yticks(())
    plt.xlim (np.min(xx), np.max(xx))
    plt.ylim (np.min(yy), np.max(yy))
    plt.savefig(filename)

(predictors, outcomes) = generate_synth_data()
# >>>predictors.shape
# >>>outcomes.shape
k=5; filename="knn_synth_5.pdf"; limits=(-3,4,-3,4); h=0.1
(xx,yy,prediction_grid) = make_prediction_grid(predictors,outcomes,limits,h,k)
plot_prediction_grid(xx,yy,prediction_grid,filename)
plt.show()