def test_KNN_predict_incorrect_shape(sample_train, sample_test):
Xtrain, ytrain = sample_train(count=500)
Xtest, ytest = sample_test(count=125)
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], -1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], -1))
knn = KNearestNeighbor()
knn.train(Xtrain,ytrain)
with pytest.raises(ValueError):
knn.predict(ytrain)#using ytrain, shich has incorrect dimensions;
def test_KNN_predict_num_loop_parameter(sample_train, sample_test, num_loops):
Xtrain, ytrain = sample_train(count=40)
Xtest, ytest = sample_test(count=10)
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], -1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], -1))
knn = KNearestNeighbor()
knn.train(Xtrain,ytrain)
with pytest.raises(ValueError):
knn.predict(Xtest,0,num_loops).shape
def test_KNN_predict_loop_parameter(sample_train, sample_test, k, num_loops):
Xtrain, ytrain = sample_train(count=40)
Xtest, ytest = sample_test(count=10)
Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], -1))
Xtest = np.reshape(Xtest, (Xtest.shape[0], -1))
knn = KNearestNeighbor()
knn.train(Xtrain,ytrain)
assert knn.predict(Xtest,k,num_loops).shape == ytest.shape
# possible value of k, run the k-nearest-neighbor algorithm num_folds times, #
# where in each case you use all but one of the folds as training data and the #
# last fold as a validation set. Store the accuracies for all fold and all #
# values of k in the k_to_accuracies dictionary. #
################################################################################
for k in k_choices:
acc = []
print(k)
for i in range(num_folds):
#(0，4000,3072),每个都是（0，1000，3072），竖着叠加，shape（4000，3072）
x_train_fold = np.vstack(X_train_folds[0:i] + X_train_folds[i + 1:])
#(，4000)，每个都是（，1000），横向叠加，shape（4000，）
y_train_fold = np.hstack((y_train_folds[0:i] + y_train_folds[i + 1:]))
x_val = X_train_folds[i]
y_val = y_train_folds[i]
classifier = KNearestNeighbor()
classifier.train(x_train_fold, y_train_fold)
dists_two = classifier.compute_distances_no_loops(x_val)
y_val_pred = classifier.predict(x_val, k)
correct = np.sum(y_val_pred == y_val) / y_val.shape[0]
acc.append(correct)
k_to_accuracies[k] = acc
################################################################################
# END OF YOUR CODE #
################################################################################
# Print out the computed accuracies
for k in sorted(k_to_accuracies):
for accuracy in k_to_accuracies[k]:
print('k = %d, accuracy = %f' % (k, accuracy))
# plot the raw observations
for k in k_choices:
accuracies = k_to_accuracies[k]
plt.scatter([k] * len(accuracies), accuracies)
# plot the trend line with error bars that correspond to standard deviation
accuracies_mean = np.array(
[np.mean(v) for k, v in sorted(k_to_accuracies.items())])
accuracies_std = np.array(
[np.std(v) for k, v in sorted(k_to_accuracies.items())])
plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)
plt.title('Cross-validation on k')
plt.xlabel('k')
plt.ylabel('Cross-validation accuracy')
plt.show()
# Based on the cross-validation results above, choose the best value for k,
# retrain the classifier using all the training data, and test it on the test
# data. You should be able to get above 28% accuracy on the test data.
best_k = 10
classifier = KNearestNeighbor()
classifier.train(X_train, y_train)
y_test_pred = classifier.predict(X_test, k=best_k)
# Compute and display the accuracy
num_correct = np.sum(y_test_pred == y_test)
accuracy = float(num_correct) / num_test
print('Got %d / %d correct => accuracy: %f' %
(num_correct, num_test, accuracy))
mask=range(num_training)
x_train=x_train[mask]
y_train=y_train[mask]
num_test=10000
mask=range(num_test)
x_test=x_test[mask]
y_test=y_test[mask]
x_train=np.reshape(x_train,(x_train.shape[0],-1))
x_test=np.reshape(x_test,(x_test.shape[0],-1))
print(x_train.shape,x_test.shape)
classifier=KNearestNeighbor()
classifier.train(x_train,y_train)
ks = range(1 , 10)
pre = []
num_correct = []
accuracy = []
for k in ks:
pr = classifier.predict(x_test , k)
num = np.sum(pr == y_test)
pre.append(pr)
num_correct.append(num)
accuracy.append(float(num) / num_test)
plt.plot(ks , accuracy)
plt.show()
# Dictionary holding the accuracies (list) for different values of k
k_to_accuracies = {}
# k-fold cross validation using fold i as validation, and all others as training
for choice in k_choices:
for i in range(num_folds):
# Partition training and test arrays
X_tr = np.vstack([X_train_folds[x] for x in range(num_folds) if x!=i])
y_tr = np.hstack([y_train_folds[x] for x in range(num_folds) if x!=i])
X_te = X_train_folds[i]
y_te = y_train_folds[i]
# Create kNN classifier instance
clf = KNearestNeighbor()
clf.train(X_tr, y_tr)
# Predict
pred = clf.predict(X_te, k=choice)
acc = float(np.sum(pred == y_te)) / y_te.shape[0]
print(f"k = {choice}, accuracy = {acc}")
if i == 0:
k_to_accuracies[choice] = [acc]
else:
k_to_accuracies[choice].append(acc)
# Plot results
for k in k_choices:
accs = k_to_accuracies[k]
plt.scatter([k] * len(accs), accs)
# Plot trend line with error bars corresponding to standard deviation
accs_mean = np.array([np.mean(val) for key,val in sorted(k_to_accuracies.items())])
accs_std = np.array([np.std(val) for key,val in sorted(k_to_accuracies.items())])
plt.errorbar(k_choices, accs_mean, yerr=accs_std)
