Xtest = Xtest.astype('float32')
model = KNN(k=3)
model.fit(X, y)
yhat = model.predict(X)
tr_error = np.mean(yhat != y)
print("k is 3", "training error is ", tr_error)
yhat = model.predict(Xvalid)
v_error = np.mean(yhat != yvalid)
print("k is 3", "validation error is ", v_error)
elif Model == "softmax":
# standardize X
X, mu, sigma = utils.standardize_cols(X)
Xvalid, _, _ = utils.standardize_cols(Xvalid, mu, sigma)
Xtest, _, _ = utils.standardize_cols(Xtest, mu, sigma)
model = softmax.softmaxClassifier()
minibatch = [500, 1000, 1500]
alpha = [0.01, 0.001, 0.0005]
min_val_error = 1
best_batch = 0
best_alpha = 0
for m in range(3):
for a in range(3):
val_error = []
# cross validation
for train, validate in kf.split(X, y):
plt.figure()
plt.scatter(X[:, f1], X[:, f2])
plt.xlabel("$x_{%d}$" % f1)
plt.ylabel("$x_{%d}$" % f2)
for i in range(n):
plt.annotate(animals[i], (X[i, f1], X[i, f2]))
utils.savefig('two_random_features.png')
elif question == '2.2':
dataset = load_dataset('animals.pkl')
X = dataset['X'].astype(float)
animals = dataset['animals']
n, d = X.shape
# standardize columns
X = utils.standardize_cols(X)
model = PCA(k=2)
model.fit(X)
Z = model.compress(X)
fig, ax = plt.subplots()
plt.ylabel('z2')
plt.xlabel('z1')
ax.scatter(Z[:, 0], Z[:, 1])
for i in range(n):
ax.annotate(animals[i], (Z[i, 0], Z[i, 1]))
utils.savefig('q2_2_PCA_animals.png')
elif question == '3.1':
X = load_dataset('highway.pkl')['X'].astype(float) / 255
parser = argparse.ArgumentParser()
parser.add_argument('-q',
'--question',
required=True,
choices=['1.2', '2.1', '3', '3.1', '3.2'])
io_args = parser.parse_args()
question = io_args.question
if question == '1.2':
dataset = utils.load_dataset('animals')
X = dataset['X'].astype(float)
animals = dataset['animals']
n, d = X.shape
k = 5
X = utils.standardize_cols(X) # standardize columns
model = PCA(k=2)
model.fit(X)
Z = model.compress(X)
# Plot the matrix
plt.imshow(Z)
utils.savefig('q1_unsatisfying_visualization_1.png')
## Randomly plot two features, and label all points
fig, ax = plt.subplots()
ax.scatter(Z[:, 0], Z[:, 1])
for i in range(n):
ax.annotate(animals[i], (Z[i, 0], Z[i, 1]))