def classify_species(s0, s1, ratio):
sc = SimpleClassifier()
iris, names = load_iris_data(s0, s1, ratio)
sc.add_data(*iris.training)
sc.train()
valr = np.array([sc.classify(x) for x in iris.training[0]])
val = np.array([sc.classify(x) for x in iris.validation[0]])
return risk(valr, iris.training[1]), risk(val, iris.validation[1])
def classify_mnist(s0, s1, ratio, usage):
sc = SimpleClassifier()
mnist, names = load_mnist_data(s0, s1, ratio, usage)
sc.k = lambda x, xp: (np.dot(x, xp))**1
sc.add_data(*mnist.training)
sc.train()
valr = np.zeros(np.shape(mnist.training[0])[0], dtype=np.int)
val = np.zeros(np.shape(mnist.validation[0])[0], dtype=np.int)
total_calcs = np.shape(mnist.training[0])[0] + np.shape(
mnist.validation[0])[0] + 1
with tqdm(total=total_calcs,
desc="Running Risk/Error Analysis",
bar_format="{l_bar}{bar} [ time left: {remaining} ]") as pbar:
for i in range(0, np.shape(mnist.training[0])[0]):
valr[i] = sc.classify(mnist.training[0][i])
pbar.update(1)
for i in range(0, np.shape(mnist.validation[0])[0]):
val[i] = sc.classify(mnist.validation[0][i])
pbar.update(1)
return risk(valr, mnist.training[1]), risk(val, mnist.validation[1])
from classifier import SimpleClassifier
inputs = [[1, 2, 1, 1, 1], [2, 1, 3, 1, 1], [1, 2, 3, 2, 3], [2, 1, 3, 2, 3]]
outputs = ['onsite_hired', 'onsite', 'rejected', 'rejected']
c = SimpleClassifier(inputs=inputs, outputs=outputs, train_eval_ratio=0.75)
print('Training model...')
c.train()
print('Evaluating model...')
accuracy = c.evaluate()
print('Model accuracy: {:.3f}'.format(accuracy))
print(c.predict([1, 1, 1, 1, 1]))
