acc_K3 = knn.KNN_test(X_train, Y_train, X_test, Y_test, 5)
print("Accuracy K=1:",acc_K1)
print("Accuracy K=3:",acc_K2)
print("Accuracy K=5:",acc_K3)
print()
best_K = knn.choose_K(X_train, Y_train, X_test, Y_test)
print("Best K:",best_K,"\n")
print()
# K-MEANS TESTING
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ First Part ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
X = np.array( [[0], [1], [2], [7], [8], [9], [12], [14], [15]] )
K = 2
C = km.K_Means(X, K)
# Visuals for debugging, Uncomment matplot header to use
print("C: \n", C)
plt.scatter(C, np.ones((C.shape[0],1)), label='centers')
plt.scatter(X, np.zeros((X.shape[0],1)), label='samples')
plt.title('X, K=2')
plt.savefig("k_means_results_0.png") #Uncomment to save plot as file
plt.show()
K_b = 3
C_b = km.K_Means(X, K_b)
# Visuals for debugging, Uncomment matplot header to use
print("C_b: \n", C_b)
plt.scatter(C_b, np.ones((C_b.shape[0],1)), label='centers')
for item in x_str:
temp = [float(x) for x in item.split(',')]
X.append(temp)
if len(y_str) > 0:
for item in y_str:
temp = int(item)
Y.append(temp)
X = np.array(X)
Y = np.array(Y)
return X, Y
X, Y = load_data("data_1.txt")
max_depth = 3
DT = dt.DT_train_binary(X, Y, max_depth)
test_acc = dt.DT_test_binary(X, Y, DT)
print("DT:", test_acc)
X, Y = load_data("data_4.txt")
acc = nn.KNN_test(X, Y, X, Y, 1)
print("KNN:", acc)
X, Y = load_data("data_5.txt")
mu = np.array([[1], [5]])
mu = kmeans.K_Means(X, 2, mu)
print("KMeans:", mu)
X, Y = load_data("data_6.txt")
mu = kmeans.K_Means(X, 2, [])
print("KMeans:", mu)
nnAcc1 = nn.KNN_test(nnTrainX1, nnTrainY1, nnTestX1, nnTestY1, 1)
print("\tK=1", "Accuracy", nnAcc1)
nnAcc2 = nn.KNN_test(nnTrainX1, nnTrainY1, nnTestX1, nnTestY1, 3)
print("\tK=3", "Accuracy", nnAcc2)
nnAcc3 = nn.KNN_test(nnTrainX1, nnTrainY1, nnTestX1, nnTestY1, 5)
print("\tK=5", "Accuracy", nnAcc3)
#writeup
nnK = nn.choose_K(nnTrainX1, nnTrainY1, nnTestX1, nnTestY1)
print("\tChoose K:", nnK)
print("\tAccuracy with chosen K:",
nn.KNN_test(nnTrainX1, nnTrainY1, nnTestX1, nnTestY1, nnK))
print()
print("Clustering")
#test
C1 = clu.K_Means(cluX1, 3)
#print(C1)
#writeup
C2 = clu.K_Means(cluX2, 2)
print("\tK=2", C2)
C3 = clu.K_Means(cluX2, 3)
print("\tK=3", C3)
#writeup
CBetter1 = clu.K_Means_better(cluX2, 2)
print("\tBetter K=2", CBetter1)
CBetter2 = clu.K_Means_better(cluX2, 3)
print("\tBetter K=3", CBetter2)
print()
print("Perceptron")
#test
import numpy as np
import clustering as cp
import matplotlib.pyplot as plt
X = np.array([[0, 1], [1, 2], [2, 3], [7, 8], [8, 9], [9, 10], [12, 13],
[14, 15], [15, 16]])
Y = np.array([[1, 0], [7, 4], [9, 6], [2, 1], [4, 8], [0, 3], [13, 5], [6, 8],
[7, 3], [3, 6], [2, 1], [8, 3], [10, 2], [3, 5], [5, 1], [1, 9],
[10, 3], [4, 1], [6, 6], [2, 2]])
K = 2
#C = cp.K_Means(X, K)
C = cp.K_Means(Y, K)
plt.matshow(X)
#plt.scatter(X[0],X[1])
#plt.scatter(C)
#plt.show()
print(C)
print("START K_MEANS_BETTER")
N = cp.K_Means_better(Y, K)
print(N)
#plt.show()