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import matplotlib.pyplot as plt

from matplotlib.colors import ListedColormap

from sklearn import neighbors

import numpy as np

def plotvector(XTrain, yTrain, XTest, yTest, weights, upperLim = 310):

results = []

for n in range(1, upperLim, 4):

clf = neighbors.KNeighborsClassifier(n_neighbors = n, weights = weights)

clf = clf.fit(XTrain, yTrain)

preds = clf.predict(XTest)

accuracy = clf.score(XTest, yTest)

results.append([n, accuracy])

results = np.array(results)

return(results)

def plotaccuracy(XTrain, yTrain, XTest, yTest, upperLim):

pltvector1 = plotvector(XTrain, yTrain, XTest, yTest, weights = "uniform", upperLim=upperLim)

pltvector2 = plotvector(XTrain, yTrain, XTest, yTest, weights = "distance", upperLim=upperLim)

line1 = plt.plot(pltvector1[:,0], pltvector1[:,1], label = "uniform")

line2 = plt.plot(pltvector2[:,0], pltvector2[:,1], label = "distance")

plt.legend(loc=3)

plt.ylim(0.5, 1)

plt.title("Accuracy with Increasing K")

plt.show()

def decisionplot(XTrain, yTrain, n_neighbors, weights):

h = .02 # step size in the mesh

Xtrain = XTrain[:, :2] # we only take the first two features.

# Create color maps

cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])

cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])

clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)

clf.fit(Xtrain, yTrain)

# Plot the decision boundary. For that, we will assign a color to each

# point in the mesh [x_min, m_max]x[y_min, y_max].

x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1

y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1

xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot

Z = Z.reshape(xx.shape)

plt.figure()

plt.pcolormesh(xx, yy, Z, cmap = cmap_light)

# Plot also the training points

plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c = yTrain, cmap = cmap_bold)

plt.xlim(xx.min(), xx.max())

plt.ylim(yy.min(), yy.max())

plt.title("2-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights))

plt.show()