From 6e5a591141af86c0a0d245c7f3e6a9a8940fca54 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Wed, 21 Sep 2022 15:38:49 +0200 Subject: [PATCH] Update week36.ipynb --- doc/pub/week36/ipynb/week36.ipynb | 86 ++++++++++++++++++++++++++++--- 1 file changed, 78 insertions(+), 8 deletions(-) diff --git a/doc/pub/week36/ipynb/week36.ipynb b/doc/pub/week36/ipynb/week36.ipynb index efa50c1ae..ed649678e 100644 --- a/doc/pub/week36/ipynb/week36.ipynb +++ b/doc/pub/week36/ipynb/week36.ipynb @@ -2314,10 +2314,43 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "fb95e6e4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta values for own Ridge implementation\n", + "[ 1.04152388 -0.08796629 -0.2265552 -0.06821103 0.04383751 0.08561268\n", + " 0.08570563 0.06773487 0.04507 0.02407463 0.00721908 -0.00497338\n", + " -0.01285634 -0.01711057 -0.0184785 -0.01764665 -0.01520244 -0.01162643\n", + " -0.00730033 -0.00252065]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[ 1.04152388 -0.08796629 -0.2265552 -0.06821103 0.04383751 0.08561268\n", + " 0.08570563 0.06773487 0.04507 0.02407463 0.00721908 -0.00497338\n", + " -0.01285634 -0.01711057 -0.0184785 -0.01764665 -0.01520244 -0.01162643\n", + " -0.00730033 -0.00252065]\n", + "MSE values for own Ridge implementation\n", + "1.621815415486309e-05\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "1.6218154154878254e-05\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -2349,10 +2382,10 @@ "p = Maxpolydegree\n", "I = np.eye(p,p)\n", "# Decide which values of lambda to use\n", - "nlambdas = 6\n", + "nlambdas = 1\n", "MSEOwnRidgePredict = np.zeros(nlambdas)\n", "MSERidgePredict = np.zeros(nlambdas)\n", - "lambdas = np.logspace(-4, 2, nlambdas)\n", + "lambdas = np.logspace(-2, 2, nlambdas)\n", "for i in range(nlambdas):\n", " lmb = lambdas[i]\n", " OwnRidgeBeta = np.linalg.pinv(X_train.T @ X_train+lmb*I) @ X_train.T @ y_train\n", @@ -2407,10 +2440,47 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "d9dbc988", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta values for own Ridge implementation\n", + "[-0.11987476 -0.17941937 -0.06692651 0.02707229 0.07073126 0.07820093\n", + " 0.06661455 0.04763463 0.02786784 0.01059519 -0.00283875 -0.01213556\n", + " -0.01751971 -0.01944576 -0.01844105 -0.0150256 -0.00967471 -0.00280481\n", + " 0.00522883]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[-0.11987476 -0.17941937 -0.06692651 0.02707229 0.07073126 0.07820093\n", + " 0.06661455 0.04763463 0.02786784 0.01059519 -0.00283875 -0.01213556\n", + " -0.01751971 -0.01944576 -0.01844105 -0.0150256 -0.00967471 -0.00280481\n", + " 0.00522883]\n", + "Intercept from own implementation:\n", + "1.0475047612229758\n", + "Intercept from Scikit-Learn Ridge implementation\n", + "1.0475047612229833\n", + "MSE values for own Ridge implementation\n", + "4.398879605532181e-05\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "4.398879605534439e-05\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -2452,11 +2522,11 @@ "p = Maxpolydegree-1\n", "I = np.eye(p,p)\n", "# Decide which values of lambda to use\n", - "nlambdas = 6\n", + "nlambdas = 1\n", "MSEOwnRidgePredict = np.zeros(nlambdas)\n", "MSERidgePredict = np.zeros(nlambdas)\n", "\n", - "lambdas = np.logspace(-4, 2, nlambdas)\n", + "lambdas = np.logspace(-2, 2, nlambdas)\n", "for i in range(nlambdas):\n", " lmb = lambdas[i]\n", " OwnRidgeBeta = np.linalg.pinv(X_train_scaled.T @ X_train_scaled+lmb*I) @ X_train_scaled.T @ (y_train_scaled)\n",