Morten Hjorth-Jensen
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
"""
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Code to test Ridge with own gradient descent and SGD
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"""
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn import linear_model
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from sklearn.neural_network import MLPRegressor
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from sklearn.metrics import accuracy_score
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import seaborn as sns
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import autograd.numpy as np
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from autograd import grad
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def MSE(y_data,y_model):
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n = np.size(y_model)
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return np.sum((y_data-y_model)**2)/n
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# A seed just to ensure that the random numbers are the same for every run.
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# Useful for eventual debugging.
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np.random.seed(315)
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n = 100
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x = np.random.rand(n)
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y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)
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Maxpolydegree = 5
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X = np.zeros((n,Maxpolydegree-1))
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for degree in range(1,Maxpolydegree): #No intercept column
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X[:,degree-1] = x**(degree)
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# We split the data in test and training data
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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nlambdas = 10
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lmbd_vals = np.logspace(-4, 0, nlambdas)
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MSERidgePredict = np.zeros(nlambdas)
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for i in range(nlambdas):
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lmb = lmbd_vals[i]
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RegRidge = linear_model.Ridge(lmb,fit_intercept=False)
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RegRidge.fit(X_train,y_train)
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ypredictRidge = RegRidge.predict(X_test)
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MSERidgePredict[i] = MSE(y_test,ypredictRidge)
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beta = np.random.randn(X_train.shape[1],1)
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loss = np.mean((y_train.reshape(-1,1) - X_train@beta)**2)
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print(loss)
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get_grad = grad(loss,argnum=2)
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grad_beta = get_grad(X_train,y_train,beta)
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