def plotUnivariate(X, y, d, reg_lambda):
model = PolynomialRegression(degree=d, reg_lambda=reg_lambda)
model.fit(X, y)
# output predictions
xpoints = np.linspace(np.max(X), np.min(X), 100).reshape(-1, 1)
ypoints = model.predict(xpoints)
# plot curve
plt.plot(X, y, 'rx')
plt.title('PolyRegression with d = ' + str(d) + ', lambda = ' +
str(reg_lambda))
plt.plot(xpoints, ypoints, 'b-')
plt.xlabel('X')
plt.ylabel('Y')
reg_lambdas = list(range(0, 50, 8))
reg_lambdas.extend([100, 1000, 10000])
# load the data, accidentaly override a python builtin keyword
filePath = "data/polydata.dat"
file = open(filePath, 'r')
allData = np.loadtxt(file, delimiter=',')
# adopt a horrible naming convention
X = allData[:, [0]]
y = allData[:, [1]]
# fit with different regression parameters and store results
xAxisData, yAxisData = [], []
for reg_lambda in reg_lambdas:
model = PolynomialRegression(degree=d, reg_lambda=reg_lambda)
model.fit(X, y)
# output predictions
xpoints = np.linspace(np.max(X), np.min(X), 100).reshape(-1, 1)
ypoints = model.predict(xpoints)
xAxisData.append(xpoints)
yAxisData.append(ypoints)
# overplot fit results
colors = cm.viridis(np.linspace(0, 1, len(yAxisData)))
fig, ax = plt.subplots()
# points
ax.plot(X, y, 'rx')
for x, y, color, label in zip(xAxisData, yAxisData, colors, reg_lambdas):
if __name__ == "__main__":
'''
Main function to test polynomial regression
'''
# load the data
filePath = "data/polydata.dat"
file = open(filePath,'r')
allData = np.loadtxt(file, delimiter=',')
X = allData[:, 0]
y = allData[:, 1]
# regression with degree = d
d = 8
model = PolynomialRegression(degree = d, regLambda = 0)
model.fit(X, y)
# output predictions
xpoints = np.linspace(np.max(X), np.min(X), 100).T
ypoints = model.predict(xpoints)
# plot curve
plt.figure()
plt.plot(X, y, 'rx')
plt.title('PolyRegression with d = '+str(d))
plt.hold(True)
plt.plot(xpoints, ypoints, 'b-')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()