def classifyPerson():
resultList = ["not at all", "in small doses", "in large doses"]
percentTats = float(input("percentage of time spent playing video games? "))
ffMiles = float(input("frequent flyer miles earned per year?" ))
iceCream = float(input("liters of ice cream consumed per year? "))
datingDataMat, datingLabels = du.fileToMatrix("/Users/brad/PycharmProjects/machineLearning/data/datingTestSet.txt")
norMat, ranges, minVals = du.autoNorm(datingDataMat)
inArr = np.asarray([ffMiles, percentTats, iceCream])
classifierResult = kNN.classify0((inArr-minVals)/ranges, norMat, datingLabels, 3)
print("You will probably like this person: ", resultList[classifierResult - 1])
def datingClassTest():
#amount of data to use for testing
hoRatio = 0.10
#convert file to matrix and labels
datingDataMat, datingLabels = du.fileToMatrix("/Users/brad/PycharmProjects/machineLearning/data/datingTestSet.txt")
#normalize the matrix
normMat, ranges, minVals = du.autoNorm(datingDataMat)
#count of observations
m = normMat.shape[0]
#count of observations for validation
numTestVecs = int(m*hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
#Loop through and test vectors 1 - amount of tests against model created with amount of tests - total
classifierResult = kNN.classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i]))
if(classifierResult != datingLabels[i]): errorCount += 1.0
print("the total error rate is: %f" % (errorCount/float(numTestVecs)))
import chapter2.dataUtility as du
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
group, labels = kNN.createDataset()
print(group)
print(labels)
print(kNN.classify0([0, 0], group, labels, 3))
print(os.path.split(__file__))
datingDataMat, datingLabels = du.fileToMatrix("/Users/brad/PycharmProjects/machineLearning/data/datingTestSet.txt")
print(datingDataMat)
print(datingLabels[0:20])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2], 15.0*np.asarray(datingLabels), 15.0*np.asarray(datingLabels))
plt.show()
normMat, ranges, minVals = du.autoNorm(datingDataMat)
print(normMat)
print(ranges)
print(minVals)