import matplotlib.pyplot as plt
import csv
import numpy as np
from knn import KNN
f = open('iris.csv')
csv_f = csv.reader(f)
print(1)
knn = KNN()
knn.Load_Dataset('iris.csv')
knn.Determine_Data_Shape('iris.csv')
print(knn.numColumnsOfData)
print(knn.numRowsOfData)
sp0 = 0
sp1 = 0
sp2 = 0
#for row in csv_f:
# if (int(row[4]) == 0):
# count += 1
#
#print("There are %s samples of Iris setosa in the sample." % count)
x = knn.data[:, 0:1]
y = knn.data[:, 1:2]
# Switched from 0:2 to :
import matplotlib.pyplot as plt
import numpy as np
from knn import KNN
import os
knn = KNN()
path = os.getcwd(
) + "/Downloads/LeapDeveloperKit_2.3.1+31549_mac/LeapSDK/lib/Del 4/iris.csv"
knn.Load_Dataset(path)
trainX = knn.data[::2, 1:3]
trainy = knn.target[::2]
testX = knn.data[1::2, 1:3]
testy = knn.target[1::2]
knn.Use_K_Of(15)
knn.Fit(trainX, trainy)
colors = np.zeros((3, 3), dtype='f')
colors[0, :] = [1, 0.5, 0.5]
colors[1, :] = [0.5, 1, 0.5]
colors[2, :] = [0.5, 0.5, 1]
plt.figure()
[numItems, numFeatures] = knn.data.shape
for i in range(0, numItems / 2):
itemClass = int(trainy[i])
currColor = colors[itemClass, :]