def plotDecisionBoundary(theta, X, y):
import numpy as np
import matplotlib.pyplot as plt
from ex2_logistic_regression.plotData import plotData
plotData(X[:, 1:], y)
m, n = np.shape(X)
if n <= 3:
min_x = min(X[:, 1])
max_x = max(X[:, 1])
x = np.array([X[:, 1].min(), X[:, 1].max()])
if n < 2:
theta[3] = 1
y = [f_y(theta, min_x), f_y(theta, max_x)]
plt.figure(1)
plt.title('Linear regression With GCD')
plt.xlabel('x')
plt.ylabel('y')
plt.scatter(x, y, marker='o', color='k', s=10, label='point')
plt.legend(loc='lower right')
plt.plot(x, y)
plt.show()
else:
from ex2_logistic_regression.mapFeature import mapFeature
x = np.linspace(-1, 1.5, 50)
y = np.linspace(-1, 1.5, 50)
z = np.zeros(shape=(len(x), len(y)))
for i in range(len(x)):
for j in range(len(y)):
z[i, j] = (mapFeature([x[i]], [y[j]]).dot(theta))
z = z.T
c = plt.contour(x, y, z, 0, origin='upper')
c.collections[0].set_label('Decision Boundary')
def test_plotData(self):
from ex2_logistic_regression.plotData import plotData
from utils import file_utils
import matplotlib.pyplot as plt
x, y = file_utils.read_csv_split_last_col(data_file_path)
plotData(x, y)
plt.title('Figure 3: Plot of traning data')
plt.xlabel('Marcochip test 1')
plt.ylabel('Marcochip test 2')
plt.show()
def test_plotData(self):
from ex2_logistic_regression.plotData import plotData
from utils import file_utils
import matplotlib.pyplot as plt
x, y = file_utils.read_csv_split_last_col(data_file_path)
plotData(x, y)
plt.title('Scatter plot of training data')
plt.xlabel('Exam 1 score')
plt.ylabel('Exam 2 score')
plt.show()
def visualizeBoundary(X, y, model):
plotData(X, y, ("Pos", "Neg"))
# Make classification predictions over a grid of values
x1plot = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), 100)
x2plot = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), 100)
X1, X2 = np.meshgrid(x1plot, x2plot)
vals = np.zeros(X1.shape)
for i in range(X1.shape[1]):
this_X = np.hstack((X1[:, i:i + 1], X2[:, i:i + 1]))
vals[:, i] = model.predict(this_X)
# Plot the SVM boundary
plt.contour(X1, X2, vals, levels=[0])
def plotDecisionBoundaryReg(theta, X, y):
plt = plotData(X, y, ('y = 1', 'y = 0'))
x1_vec, x2_vec = np.meshgrid(np.linspace(-1, 1.5), np.linspace(-1, 1.5))
hypothesis = sigmoid(np.dot(mapFeature(x1_vec.flatten(), x2_vec.flatten()), theta)).reshape(x1_vec.shape)
plt.contour(x1_vec, x2_vec, hypothesis, levels=0.5, colors='g')
plt.show()
return
from ex6_support_vector_machines.gaussianKernel import gaussianKernel
from ex6_support_vector_machines.visualizeBoundary import visualizeBoundary
from ex6_support_vector_machines.dataset3Params import dataset3Params
print('Loading and Visualizing Data ...\n')
# % Load from ex6data1:
# % You will have X, y in your environment
mat = io.loadmat('ex6data1.mat')
X, y = (
mat['X'],
mat['y'],
)
# % Plot training data
plotData(X, y, ("Pos", "Neg"))
plt.show()
# fprintf('Program paused. Press enter to continue.\n');
# pause;
#
# %% ==================== Part 2: Training Linear SVM ====================
# % The following code will train a linear SVM on the dataset and plot the
# % decision boundary learned.
# %
#
# % Load from ex6data1:
# % You will have X, y in your environment
# load('ex6data1.mat');
#
print('\nTraining Linear SVM ...\n')