Python RidgeRegression Beispiele

Beispiel #1

import numpy as np
import datasets
import regression
import importlib

X, Y = datasets.load_nonlinear_example1()
ex_X = datasets.polynomial3_features(X)

samples = np.arange(0, 4, 0.1)
x_samples = np.c_[ np.ones(len(samples)), samples ]
ex_x_samples = datasets.polynomial3_features(x_samples)

list = [0, 0.1, 0.5, 1.0, 10]

import matplotlib.pyplot as plt
plt.scatter(X[:,1], Y)

for x in list:
    model = regression.RidgeRegression(x)
    model.fit(ex_X, Y)
    plt.plot(samples, model.predict(ex_x_samples))

plt.show()

Beispiel #2

Datei: main.py Projekt: matsuda-kazuhide/dm_week4

import datasets
X, Y = datasets.load_nonlinear_example1()
ex_X = datasets.polynomial3_features(X)
print(ex_X)
print(X[0])
print(Y)

import regression
model = regression.RidgeRegression(alpha=0.5)
model = regression.RidgeRegression()
print(model.alpha)

import importlib
importlib.reload(regression)
model = regression.LinearRegression()
model.fit(ex_X, Y)
print(model.theta)

print(model.predict(ex_X))

print(model.score(ex_X, Y))

Beispiel #3

Datei: pipeline.py Projekt: dariopa/Intro-to-ML

    trafo = preprocessing.task1btransformation()
    X_train = trafo.transform(X_train)
    if BFinalPrediction == 0:
        X_test = trafo.transform(X_test)

## Linear Regression
if BLinearRegression == 1:
    LinReg = regression.LinearRegression()
    LinReg.fit(X_train, y_train)
    if BFinalPrediction == 0:
        y_pred = LinReg.predict(X_test)
    w = LinReg.getcoeff()

if BRidgeRegression == 1:
    l = 15
    RidgeReg = regression.RidgeRegression(alpha=l)
    RidgeReg.fit(X_train, y_train)
    if BFinalPrediction == 0:
        y_pred = RidgeReg.predict(X_test)
    w = RidgeReg.getcoeff()

if BLassoRegression == 1:
    l = 1
    LassoReg = regression.LassoRegression(alpha=l)
    LassoReg.fit(X_train, y_train)
    if BFinalPrediction == 0:
        y_pred = LassoReg.predict(X_test)
    w = LassoReg.getcoeff()

## SVM
if BSVClassification == 1:

Beispiel #4

Datei: after.py Projekt: e185715/dm_week4

import regression
import datasets
import numpy as np

import matplotlib.pyplot as plt

alpha = [0, 0.1, 0.5, 1.0, 10.0]

importlib.reload(regression)
X, Y = datasets.load_nonlinear_example1()
ex_X = datasets.polynomial3_features(X)

#model = regression.RidgeRegression(alpha=0)
#model = regression.RidgeRegression()
#model.fit(ex_X,Y)

#print(model.theta)
#print(model.predict(ex_X))
#print(model.score(ex_X,Y))
samples = np.arange(0, 4, 0.1)

plt.scatter(X[:, 1], Y)
for i in alpha:
    model = regression.RidgeRegression(alpha=i)
    model.fit(ex_X, Y)
    x_samples = np.c_[np.ones(len(samples)), samples]
    ex_x_samples = datasets.polynomial3_features(x_samples)
    plt.plot(samples, model.predict(ex_x_samples), label="a=" + str(i))
    plt.legend(loc=0)
plt.show()