used_time = time.time() - start_time
print(
"KD Tree using cosine distance with resubstitution method querying time is %s seconds"
% used_time)
cfm_rs = confusion_matrix(Y, predicted_rs, labels=range(1, 11))
print(
"KD Tree using cosine distance with resubstitution method confusion matrix is "
)
print(cfm_rs)
ac_rate = accuracy_score(Y, predicted_rs)
print(
"KD Tree using cosine distance with resubstitution method accurary score is %s"
% ac_rate)
plt.figure()
plot_confusion_matrix(
cfm_rs,
classes=range(1, 11),
title='KD Tree using cosine distance with resubstitution method')
k = 20
kf = KFold(n_splits=k, shuffle=True)
kf.get_n_splits(X)
cfm_kfold = np.zeros(shape=(
10,
10,
), dtype=np.int64)
i = 1
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = Y[train_index], Y[test_index]
clf_KFold = KNeighborsClassifier(n_neighbors=5,
algorithm='kd_tree',
print(
"Linear Search using euclidean distance with resubstitution method querying time is %s seconds"
% used_time)
cfm_rs = confusion_matrix(Y, predicted_rs, labels=range(1, 11))
print(
"Linear Search using euclidean distance with resubstitution method confusion matrix is "
)
print(cfm_rs)
ac_rate = accuracy_score(Y, predicted_rs)
print(
"Linear Search using euclidean distance with resubstitution method accurary score is %s"
% ac_rate)
plt.figure()
plot_confusion_matrix(
cfm_rs,
classes=range(1, 11),
title=
'Linear Search using euclidean distance with resubstitution method')
k = 20
kf = KFold(n_splits=k, shuffle=True)
kf.get_n_splits(X)
cfm_kfold = np.zeros(shape=(
10,
10,
), dtype=np.int64)
i = 1
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = Y[train_index], Y[test_index]
clf_KFold = KNeighborsClassifier(n_neighbors=5,