def test_invalid_class():
ppn = Perceptron(epochs=40, eta=0.01, random_seed=1)
try:
ppn.fit(X, y2) # -2, 1 class
assert (1 == 2)
except ValueError:
pass
def test_standardized_iris_data():
t1 = np.array([0.18, 0.41, 0.50])
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn.fit(X_std, y1) # -1, 1 class
assert ((y1 == ppn.predict(X_std)).all())
def test_standardized_iris_data():
t1 = np.array([0.18, 0.41, 0.50])
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn.fit(X_std, y1) # -1, 1 class
assert((y1 == ppn.predict(X_std)).all())
def test_invalid_class():
ppn = Perceptron(epochs=40, eta=0.01, random_seed=1)
try:
ppn.fit(X, y2) # -2, 1 class
assert 1 == 2
except ValueError:
pass
def __init__(self, eta=0.1, epochs=50, random_seed=None, print_progress=0):
epochs = int(epochs)
warnings.filterwarnings(module='mlxtend*',
action='ignore',
category=FutureWarning)
_Perceptron.__init__(self, eta, epochs, random_seed, print_progress)
BaseWrapperClf.__init__(self)
def test_invalid_labels_2():
y1 = np.where(y0 == 0, -1, 1)
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
assert_raises(AttributeError,
'y array must not contain negative labels.\nFound [-1 1]',
ppn.fit, X, y1, {(-1, 1)})
def test_invalid_labels_1():
y1 = np.where(y0 == 0, 2, 1)
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
assert_raises(AttributeError,
'Labels not in {(0, 1)}.\nFound (1, 2)',
ppn.fit,
X,
y1,
{(0, 1)})
def test_invalid_labels_1():
y1 = np.where(y0 == 0, 2, 1)
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
if sys.version_info >= (3, 0):
objtype = '{(0, 1)}'
else:
objtype = 'set([(0, 1)])'
expect = 'Labels not in %s.\nFound (1, 2)' % objtype
assert_raises(AttributeError, expect, ppn.fit, X, y1, {(0, 1)})
def test_0_1_class_iris_data():
t1 = np.array([0.26, -0. , 0.27])
ppn = Perceptron(epochs=40, eta=0.01, random_seed=1)
ppn.fit(X, y0) # 0, 1 class
assert((y0 == ppn.predict(X)).all())
from mlxtend.evaluate import plot_decision_regions #! ImportError: cannot import name 'plot_decision_regions'
from mlxtend.classifier import Perceptron
from mlxtend.classifier import Adaline
from mlxtend.classifier import MultiLayerPerceptron
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
import matplotlib.gridspec as gridspec
import itertools
gs = gridspec.GridSpec(2, 2) #xw
X, y = make_moons(n_samples=100, random_state=123)
fig = plt.figure(figsize=(10, 8))
ppn = Perceptron(epochs=50, eta=0.05, random_seed=0)
ppn.fit(X, y)
ada = Adaline(epochs=50, eta=0.05, random_seed=0)
ada.fit(X, y)
mlp = MultiLayerPerceptron(n_output=len(np.unique(y)),
n_features=X.shape[1],
n_hidden=150,
l2=0.0,
l1=0.0,
epochs=500,
eta=0.01,
alpha=0.0,
decrease_const=0.0,
minibatches=1,
shuffle_init=False,
# Author: Sebastian Raschka <sebastianraschka.com>
#
# License: BSD 3 clause
from mlxtend.classifier import Perceptron
from mlxtend.data import iris_data
import numpy as np
from nose.tools import raises
# Iris Data
X, y = iris_data()
X = X[:, [0, 3]] # sepal length and petal width
X = X[0:100] # class 0 and class 1
y0 = y[0:100] # class 0 and class 1
y1 = np.where(y[0:100] == 0, -1, 1) # class -1 and class 1
y2 = np.where(y[0:100] == 0, -2, 1) # class -2 and class 1
# standardize
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn = ppn.fit(X_std, y1) # -1, 1 class
assert((y1 == ppn.predict(X_std)).all())
test_standardized_iris_data()
def test_array_dimensions():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn = ppn.fit(np.array([1, 2, 3]), [-1])
def test_standardized_iris_data_with_zero_weights():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, zero_init_weight=True)
ppn = ppn.fit(X_std, y1) # -1, 1 class
assert (y1 == ppn.predict(X_std)).all()
def test_progress_3():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, print_progress=3)
ppn = ppn.fit(X_std, y0)
def test_nonstandardized_iris_data():
ppn = Perceptron(epochs=100, eta=0.01, random_seed=1)
ppn = ppn.fit(X, y0)
assert (y0 == ppn.predict(X)).all()
# Loading Data
X, y = iris_data()
X = X[:, [0, 3]] # sepal length and petal width
X = X[0:100] # class 0 and class 1
y = y[0:100] # class 0 and class 1
# standardize
X[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
# Note that this implementation of the Perceptron expects binary class labels in {0, 1}.
# Rosenblatt Perceptron
ppn = Perceptron(
epochs=5, # num of passes, default 50
eta=0.05, # learning rate 0.0 ~ 1.0, default 0.1
random_seed=0,
print_progress=3)
ppn.fit(X, y)
plot_decision_regions(X, y, clf=ppn)
plt.title('Perceptron - Rosenblatt Perceptron Rule')
plt.show()
print('Bias & Weights: %s' % ppn.w_)
plt.plot(range(len(ppn.cost_)), ppn.cost_)
plt.xlabel('Iterations')
plt.ylabel('Missclassifications')
plt.show()
def test_nonstandardized_iris_data():
t1 = np.array([0.078, -0.074, 0.46])
ppn = Perceptron(epochs=40, eta=0.01, random_seed=1)
ppn.fit(X, y1) # -1, 1 class
assert ((y1 == ppn.predict(X)).all())
def test_nonstandardized_iris_data():
t1 = np.array([0.078, -0.074, 0.46])
ppn = Perceptron(epochs=40, eta=0.01, random_seed=1)
ppn.fit(X, y1) # -1, 1 class
assert((y1 == ppn.predict(X)).all())
def test_score_function():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, shuffle=True)
ppn = ppn.fit(X_std, y1) # -1, 1 class
acc = ppn.score(X_std, y1)
assert acc == 1.0, acc
def test_ary_persistency_in_shuffling():
orig = X.copy()
ppn = Perceptron(epochs=40, eta=0.05, random_seed=1)
ppn = ppn.fit(X, y0) # 0, 1 class
np.testing.assert_almost_equal(orig, X, 6)
# Loading Data
X, y = iris_data()
X = X[:, [0, 3]] # sepal length and petal width
X = X[0:100] # class 0 and class 1
y = y[0:100] # class 0 and class 1
# standardize
X[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std()
X[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std()
# Note that this implementation of the Perceptron expects binary class labels in {0, 1}.
# Rosenblatt Perceptron
ppn = Perceptron(epochs=5, # num of passes, default 50
eta=0.05, # learning rate 0.0 ~ 1.0, default 0.1
random_seed=0,
print_progress=3)
ppn.fit(X, y)
plot_decision_regions(X, y, clf=ppn)
plt.title('Perceptron - Rosenblatt Perceptron Rule')
plt.show()
print('Bias & Weights: %s' % ppn.w_)
plt.plot(range(len(ppn.cost_)), ppn.cost_)
plt.xlabel('Iterations')
plt.ylabel('Missclassifications')
plt.show()
def test_array_dimensions():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn = ppn.fit(np.array([1, 2, 3]), [-1])
def test_standardized_iris_data():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn = ppn.fit(X_std, y0)
assert (y0 == ppn.predict(X_std)).all(), ppn.predict(X_std)
def test_standardized_iris_data_with_shuffle():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, shuffle=True)
ppn = ppn.fit(X_std, y1) # -1, 1 class
assert (y1 == ppn.predict(X_std)).all()
def test_score_function():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn = ppn.fit(X_std, y0)
acc = ppn.score(X_std, y0)
assert acc == 1.0, acc
def test_score_function():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, shuffle=True)
ppn = ppn.fit(X_std, y1) # -1, 1 class
acc = ppn.score(X_std, y1)
assert acc == 1.0, acc
def test_clone():
ppn = Perceptron()
clone(ppn)
def test_standardized_iris_data_with_zero_weights():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, zero_init_weight=True)
ppn = ppn.fit(X_std, y1) # -1, 1 class
assert (y1 == ppn.predict(X_std)).all()
def test_standardized_iris_data_with_shuffle():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, shuffle=True)
ppn = ppn.fit(X_std, y1) # -1, 1 class
assert (y1 == ppn.predict(X_std)).all()
def test_0_1_class_iris_data():
ppn = Perceptron(epochs=40, eta=0.05, random_seed=1)
ppn = ppn.fit(X, y0) # 0, 1 class
print(y0)
print(ppn.predict(X))
assert (y0 == ppn.predict(X)).all()
def test_0_1_class_iris_data():
ppn = Perceptron(epochs=40, eta=0.05, random_seed=1)
ppn = ppn.fit(X, y0) # 0, 1 class
print(y0)
print(ppn.predict(X))
assert (y0 == ppn.predict(X)).all()
def test_progress_3():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1, print_progress=3)
ppn = ppn.fit(X_std, y0)
from mlxtend.data import iris_data
from mlxtend.plotting import plot_decision_regions
from mlxtend.classifier import Perceptron
import matplotlib.pyplot as plt
import numpy as np
# Loading Data
X, y = iris_data()
X = X[:, [0, 3]] # sepal length and petal width
#print(X)
X = X[0:100] # class 0 and class 1
print(X.shape)
y = y[0:100] # class 0 and class 1
print(y.shape)
# standardize
X[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std()
X[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std()
print(X)
# Rosenblatt Perceptron
ppn = Perceptron(epochs=5,
eta=0.05,
random_seed=0,
print_progress=3)
ppn.fit(X, y)
x2=np.array([[0.35866332 ,0.91401319],[5.7,1.3]])
print("\n",ppn.predict(x2))
def test_nonstandardized_iris_data():
ppn = Perceptron(epochs=100, eta=0.01, random_seed=1)
ppn = ppn.fit(X, y0)
assert (y0 == ppn.predict(X)).all()
def test_standardized_iris_data():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn = ppn.fit(X_std, y0)
assert (y0 == ppn.predict(X_std)).all(), ppn.predict(X_std)
def test_0_1_class_iris_data():
t1 = np.array([0.26, -0., 0.27])
ppn = Perceptron(epochs=40, eta=0.01, random_seed=1)
ppn.fit(X, y0) # 0, 1 class
assert ((y0 == ppn.predict(X)).all())
def test_score_function():
ppn = Perceptron(epochs=15, eta=0.01, random_seed=1)
ppn = ppn.fit(X_std, y0)
acc = ppn.score(X_std, y0)
assert acc == 1.0, acc
def test_ary_persistency_in_shuffling():
orig = X.copy()
ppn = Perceptron(epochs=40, eta=0.05, random_seed=1)
ppn = ppn.fit(X, y0) # 0, 1 class
np.testing.assert_almost_equal(orig, X, 6)
# Loading Data
X, y = wine_data()
X = X[:, [3, 12]] # hue, ash
X = X[0:100] # class 0 and class 1
y = y[0:100] # class 0 and class 1
# standardize
X[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std()
X[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std()
# Rosenblatt Perceptron
ppn = Perceptron(epochs=500,
eta=0.05,
random_seed=1,
print_progress=3)
ppn.fit(X, y)
plot_decision_regions(X, y, clf=ppn)
plt.title('Wine Data - Hue and Ash 500 Epochs')
plt.show()
print('Bias & Weights: %s' % ppn.w_)
plt.plot(range(len(ppn.cost_)), ppn.cost_)
plt.xlabel('Iterations')
plt.ylabel('Missclassifications')
plt.show()
def identificador(self):
w_prueba = []
Elementos = [self.word, self.comparar]
auxiliar_contador = 0
for j in Elementos:
if len(self.word) <= len(j):
for i in range(len(self.word)):
if self.word[i] == j[i]:
w_prueba.append(1)
else:
w_prueba.append(0)
else:
for i in range(len(j)):
if self.word[i] == j[i]:
w_prueba.append(1)
else:
w_prueba.append(0)
#print (w_prueba)
#print(sum(w_prueba[:len(self.word)]))
auxiliar_x = sum(w_prueba[:len(self.word)])
#print(auxiliar_x)
auxiliar_x2 = sum(w_prueba[len(self.word):len(self.word) +
len(self.comparar)])
#print(auxiliar_x2)
X = np.array([[len(self.word),
sum(w_prueba[:len(self.word)])],
[len(self.word),
sum(w_prueba[:len(self.word)])],
[len(self.word),
sum(w_prueba[1:len(self.word)])],
[len(self.word),
sum(w_prueba[1:len(self.word)])],
[len(self.word) + 1,
len(self.word) - 1],
[len(self.word) - 1,
len(self.word) - 1],
[len(self.word) + 1,
len(self.word) - 2],
[len(self.word) - 1,
len(self.word) - 2]])
#print(X[:,0])
X[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
#print(X)
#print(y)
ppn = Perceptron(epochs=5, eta=0.05, random_seed=0, print_progress=3)
ppn.fit(X, y)
X2 = (np.array([[len(self.word), auxiliar_x],
[len(self.word) + 1,
len(self.word) - 1],
[len(self.comparar), auxiliar_x2]]))
#print("\n",X2[:,0].std())
#print("\n",X2[:,1].std())
X2[:, 0] = (X2[:, 0] - X2[:, 0].mean()) / X2[:, 0].std()
X2[:, 1] = (X2[:, 1] - X2[:, 1].mean()) / X2[:, 1].std()
#print(X2)
#print("\n",ppn.predict(X2))
resultado = ppn.predict(X2)
self.encontro = resultado[2]
print("\n\n")
a = os.system("clear")
return self.encontro
