Update week35.ipynb
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@@ -1205,7 +1205,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 2,
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"id": "b550fbbe",
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"metadata": {},
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"outputs": [
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@@ -1213,7 +1213,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 2.00000000e+00 -5.21804822e-15 5.00000000e+00]\n"
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"[ 1.93918379 1.05631924 1.04975077 5.1874182 -2.22521131]\n"
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]
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}
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],
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@@ -1222,15 +1222,15 @@
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"# First we set up the data\n",
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"import numpy as np\n",
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"x = np.random.rand(100)\n",
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"y = 2.0+5*x*x#+0.1*np.random.randn(100)\n",
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"y = 2.0+5*x*x+0.1*np.random.randn(100)\n",
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"# and then the design matrix X including the intercept\n",
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"# The design matrix now as function of a fourth-order polynomial\n",
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"X = np.zeros((len(x),3))\n",
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"X = np.zeros((len(x),5))\n",
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"X[:,0] = 1.0\n",
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"X[:,1] = x\n",
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"X[:,2] = x**2\n",
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"#X[:,3] = x**3\n",
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"#X[:,4] = x**4\n",
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"X[:,3] = x**3\n",
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"X[:,4] = x**4\n",
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"beta = (np.linalg.inv(X.T @ X) @ X.T ) @ y\n",
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"print(beta)\n",
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"# and then make the prediction\n",
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@@ -1269,7 +1269,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 3,
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"id": "ae3d0b17",
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"metadata": {},
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"outputs": [],
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@@ -1288,7 +1288,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 4,
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"id": "9b073a72",
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"metadata": {},
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"outputs": [
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@@ -1296,7 +1296,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.0\n"
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"0.9951013500487981\n"
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]
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}
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],
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@@ -1314,7 +1314,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 5,
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"id": "7ad18065",
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"metadata": {},
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"outputs": [
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@@ -1322,7 +1322,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.9895184688083084e-22\n"
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"0.010861520579271734\n"
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]
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}
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],
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@@ -1643,7 +1643,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 7,
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"id": "f48cac43",
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"metadata": {},
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"outputs": [
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