class Program():
"""
Object responsible for running the program and controlling the overall
program flow.
"""
def __init__(self):
np.random.seed(1)
gamma = Gamma()
gamma.set_estimators(2)
self.classifier = BayesClassifier(2)
self.classifier.add_class(Normal(), 0)
self.classifier.add_class(gamma, 1)
self.main()
def main(self):
"""
Overall main function of the program. Generates data, trains model
and measure accuracy of the model.
"""
x_train, y_train = self.generate_normal_data(600)
x_test, y_test = self.generate_normal_data(300, plot=False)
self.classifier.fit(x_train, y_train)
acc = self.classifier.accuracy(x_test, y_test)
print("accuracy: ", acc)
def generate_normal_data(self, N, plot=True):
"""
Generate N data samples of normal distributed data for 1 class and
gamma distributed data from 1 class.
param N: total number of samples to generate
type N: int
param plot: True if the dataset is to be plotted
type plot: bool, optional
return: tuple of dataset and corresponding labels
rtype: tuple
"""
mu = 23
sigma = 5
shape = 2
scale = 2
x1 = np.random.normal(mu, sigma, int(N / 3))
x2 = np.random.gamma(scale, shape, int(N / 3))
x = np.concatenate((x1, x2))
y = np.concatenate((np.zeros(int(N / 3)), np.ones(int(N / 3))))
if plot:
plt.hist(x2, 50, density=True)
plt.hist(x1, 50, density=True)
plt.show()
return x, y
class Program():
"""
Object responsible for running the program and controlling the overall
program flow.
"""
def __init__(self):
np.random.seed(1)
self.classifier = BayesClassifier(3)
self.classifier.add_class(Normal(), 0)
self.classifier.add_class(Normal(), 1)
self.classifier.add_class(Normal(), 2)
self.main()
def main(self):
"""
Overall main function of the program. Generates data, trains model
and measure accuracy of the model.
"""
x_train, y_train = self.generate_normal_data(60)
x_test, y_test = self.generate_normal_data(30, plot=False)
self.classifier.fit(x_train, y_train)
acc = self.classifier.accuracy(x_test, y_test)
print("accuracy: ", acc)
def generate_normal_data(self, N, plot=True):
"""
Generate N data samples of normal distributed data for 3 classes.
param N: total number of samples to generate
type N: int
param plot: True if the dataset is to be plotted
type plot: bool, optional
return: tuple of dataset and corresponding labels
rtype: tuple
"""
mu1 = 2.5
mu2 = 4.3
mu3 = 6.2
sigma1 = 0.3
sigma2 = 0.5
sigma3 = 0.9
x1 = np.random.normal(mu1, sigma1, int(N / 3))
x2 = np.random.normal(mu2, sigma2, int(N / 3))
x3 = np.random.normal(mu3, sigma3, int(N / 3))
x = np.concatenate((x1, x2, x3))
y = np.concatenate(
(np.zeros(int(N / 3)), np.ones(int(N / 3)), np.full((int(N / 3)),
2)))
if plot:
plt.figure(1, figsize=(8, 8))
plt.scatter(x1,
np.zeros(x1.shape),
s=120,
facecolors='none',
edgecolors='black',
linewidth=3.0,
label='Class 1')
plt.scatter(x2,
np.zeros(x2.shape),
s=120,
facecolors='none',
edgecolors='blue',
linewidth=3.0,
label='Class 2')
plt.scatter(x3,
np.zeros(x3.shape),
s=120,
facecolors='none',
edgecolors='red',
linewidth=3.0,
label='Class 3')
plt.legend()
plt.show()
return x, y
class Program():
"""
Object responsible for running the program and controlling the overall
program flow.
"""
def __init__(self):
self.classifier = BayesClassifier(2)
self.classifier.add_class(MultivariateNormal(), 0)
self.classifier.add_class(MultivariateNormal(), 1)
self.main()
def main(self):
"""
Overall main function of the program. Generates data, trains model
and measure accuracy of the model.
"""
x_train, y_train = self.generate_multivariate_data(100, plot=True)
x_test, y_test = self.generate_multivariate_data(50, plot=True)
self.classifier.fit(x_train, y_train)
acc = self.classifier.accuracy(x_test, y_test)
print("accuracy: ", acc)
def generate_multivariate_data(self, N, plot=True):
"""
Generate N multivariate gaussian samples
param N: total number of samples to generate
type N: int
param plot: True if the dataset is to be plotted
type plot: bool, optional
return: tuple of dataset and corresponding labels
rtype: tuple
"""
mu1 = np.array([1, 1])
mu2 = np.array([2.0, 2.0])
sigma = np.array([[0.2, 0.0], [0.0, 0.2]])
x1 = np.random.multivariate_normal(mu1, sigma, int(N / 2))
x2 = np.random.multivariate_normal(mu2, sigma, int(N / 2))
x = np.concatenate((x1, x2))
y = np.concatenate((np.zeros(int(N / 2)), np.ones(int(N / 2))))
if plot:
plt.figure(1, figsize=(8, 8))
plt.scatter(x1[:, 0],
x1[:, 1],
s=120,
facecolors='none',
edgecolors='black',
linewidth=3.0,
label='Class 1')
plt.scatter(x2[:, 0],
x2[:, 1],
s=120,
facecolors='none',
edgecolors='blue',
linewidth=3.0,
label='Class 2')
plt.legend()
plt.show()
return x, y