Clarify the 'not in the book' comments
Aurélien Geron
parent
633436e8ae
commit
dc64daaf65
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@ -152,7 +152,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – generates and saves Figure 4–1\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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@ -207,11 +207,11 @@
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.figure(figsize=(6, 4)) # not in the book\n",
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"plt.figure(figsize=(6, 4)) # not in the book – not needed, just formatting\n",
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"plt.plot(X_new, y_predict, \"r-\", label=\"Predictions\")\n",
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"plt.plot(X, y, \"b.\")\n",
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"\n",
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"# not in the book\n",
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"# not in the book – beautifies and saves Figure 4–2\n",
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"plt.xlabel(\"$x_1$\")\n",
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"plt.ylabel(\"$y$\", rotation=0)\n",
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"plt.axis([0, 2, 0, 15])\n",
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@ -325,7 +325,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – generates and saves Figure 4–8\n",
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"\n",
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"import matplotlib as mpl\n",
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"\n",
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@ -347,16 +347,7 @@
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" plt.axis([0, 2, 0, 15])\n",
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" plt.grid()\n",
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" plt.title(r\"$\\eta = {}$\".format(eta))\n",
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" return theta_path"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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" return theta_path\n",
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"\n",
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"np.random.seed(42)\n",
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"theta = np.random.randn(2,1) # random initialization\n",
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@ -384,20 +375,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"\n",
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"# we need to store the path of theta in the parameter space to plot the next\n",
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"# figure\n",
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"theta_path_sgd = []"
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"theta_path_sgd = [] # not in the book – we need to store the path of theta in\n",
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" # the parameter space to plot the next figure"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -410,14 +398,13 @@
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"np.random.seed(42)\n",
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"theta = np.random.randn(2, 1) # random initialization\n",
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"\n",
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"# not in the book, this is for the next figure\n",
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"n_shown = 20\n",
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"plt.figure(figsize=(6, 4))\n",
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"n_shown = 20 # not in the book – just needed to generate the figure below\n",
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"plt.figure(figsize=(6, 4)) # not in the book – not needed, just formatting\n",
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"\n",
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"for epoch in range(n_epochs):\n",
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" for iteration in range(m):\n",
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"\n",
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" # not in the book\n",
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" # not in the book – these 4 lines are used to generate the figure\n",
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" if epoch == 0 and iteration < n_shown:\n",
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" y_predict = X_new_b @ theta\n",
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" color = mpl.colors.rgb2hex(plt.cm.OrRd(iteration / n_shown + 0.15))\n",
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@ -429,9 +416,9 @@
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" gradients = 2 / 1 * xi.T @ (xi @ theta - yi)\n",
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" eta = learning_schedule(epoch * m + iteration)\n",
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" theta = theta - eta * gradients\n",
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" theta_path_sgd.append(theta) # not in the book\n",
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" theta_path_sgd.append(theta) # not in the book – to generate the figure\n",
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"\n",
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"# not in the book\n",
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"# not in the book – this section beautifies and saves Figure 4–10\n",
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"plt.plot(X, y, \"b.\")\n",
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"plt.xlabel(\"$x_1$\")\n",
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"plt.ylabel(\"$y$\", rotation=0)\n",
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@ -443,7 +430,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 20,
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"metadata": {
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"scrolled": true
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},
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@ -454,7 +441,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -467,7 +454,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -490,10 +477,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this cell generates and saves Figure 4–11\n",
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"\n",
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"from math import ceil\n",
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"\n",
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"n_epochs = 50\n",
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@ -520,35 +509,12 @@
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" gradients = 2 / minibatch_size * xi.T @ (xi @ theta - yi)\n",
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" eta = learning_schedule(iteration)\n",
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" theta = theta - eta * gradients\n",
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" theta_path_mgd.append(theta)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"theta"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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" theta_path_mgd.append(theta)\n",
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"\n",
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"theta_path_bgd = np.array(theta_path_bgd)\n",
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"theta_path_sgd = np.array(theta_path_sgd)\n",
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"theta_path_mgd = np.array(theta_path_mgd)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"theta_path_mgd = np.array(theta_path_mgd)\n",
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"\n",
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"plt.figure(figsize=(7, 4))\n",
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"plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], \"r-s\", linewidth=1,\n",
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" label=\"Stochastic\")\n",
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@ -574,7 +540,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -586,11 +552,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 4–12\n",
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"plt.figure(figsize=(6, 4))\n",
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"plt.plot(X, y, \"b.\")\n",
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"plt.xlabel(\"$x_1$\")\n",
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@ -603,7 +569,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -616,7 +582,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -625,7 +591,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -636,11 +602,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 4–13\n",
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"\n",
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"X_new = np.linspace(-3, 3, 100).reshape(100, 1)\n",
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"X_new_poly = poly_features.transform(X_new)\n",
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@ -660,11 +626,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 4–14\n",
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"\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.pipeline import make_pipeline\n",
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@ -700,7 +666,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -712,11 +678,11 @@
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"train_errors = -train_scores.mean(axis=1)\n",
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"valid_errors = -valid_scores.mean(axis=1)\n",
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"\n",
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"plt.figure(figsize=(6, 4)) # not in the book\n",
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"plt.figure(figsize=(6, 4)) # not in the book – not need, just formatting\n",
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"plt.plot(train_sizes, train_errors, \"r-+\", linewidth=2, label=\"train\")\n",
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"plt.plot(train_sizes, valid_errors, \"b-\", linewidth=3, label=\"valid\")\n",
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"\n",
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"# not in the book: beautifies and saves the figure\n",
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"# not in the book – beautifies and saves Figure 4–15\n",
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"plt.xlabel(\"Training set size\")\n",
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"plt.ylabel(\"RMSE\")\n",
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"plt.grid()\n",
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@ -729,7 +695,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -741,11 +707,20 @@
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"\n",
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"train_sizes, train_scores, valid_scores = learning_curve(\n",
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" polynomial_regression, X, y, train_sizes=np.linspace(0.01, 1.0, 40), cv=5,\n",
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" scoring=\"neg_root_mean_squared_error\")\n",
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" scoring=\"neg_root_mean_squared_error\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – generates and saves Figure 4–16\n",
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"\n",
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"# not in the book (same as earlier)\n",
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"train_errors = -train_scores.mean(axis=1)\n",
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"valid_errors = -valid_scores.mean(axis=1)\n",
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"\n",
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"plt.figure(figsize=(6, 4))\n",
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"plt.plot(train_sizes, train_errors, \"r-+\", linewidth=2, label=\"train\")\n",
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"plt.plot(train_sizes, valid_errors, \"b-\", linewidth=3, label=\"valid\")\n",
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@ -781,11 +756,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – we've done this type of generation several times before\n",
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"np.random.seed(42)\n",
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"m = 20\n",
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"X = 3 * np.random.rand(m, 1)\n",
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@ -795,11 +770,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – a quick peek at the dataset we just generated\n",
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"plt.figure(figsize=(6, 4))\n",
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"plt.plot(X, y, \".\")\n",
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"plt.xlabel(\"$x_1$\")\n",
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@ -811,7 +786,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -824,11 +799,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 4–17\n",
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"\n",
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"def plot_model(model_class, polynomial, alphas, **model_kargs):\n",
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" plt.plot(X, y, \"b.\", linewidth=3)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – show that we get roughly the same solution as earlier when\n",
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"# we use Stochastic Average GD (solver=\"sag\")\n",
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"ridge_reg = Ridge(alpha=1, solver=\"sag\", random_state=42)\n",
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"ridge_reg.fit(X, y)\n",
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"ridge_reg.predict([[1.5]])"
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@ -894,7 +870,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -907,11 +883,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 4–18\n",
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"plt.figure(figsize=(9, 3.5))\n",
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"plt.subplot(121)\n",
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"plot_model(Lasso, polynomial=False, alphas=(0, 0.1, 1), random_state=42)\n",
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@ -925,10 +901,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this BIG cell generates and saves Figure 4–19\n",
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"\n",
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"t1a, t1b, t2a, t2b = -1, 3, -1.5, 1.5\n",
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"\n",
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"t1s = np.linspace(t1a, t1b, 500)\n",
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@ -946,15 +924,8 @@
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"t_min_idx = np.unravel_index(J.argmin(), J.shape)\n",
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"t1_min, t2_min = t1[t_min_idx], t2[t_min_idx]\n",
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"\n",
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"t_init = np.array([[0.25], [-1]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"t_init = np.array([[0.25], [-1]])\n",
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"\n",
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"def bgd_path(theta, X, y, l1, l2, core=1, eta=0.05, n_iterations=200):\n",
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" path = [theta]\n",
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" for iteration in range(n_iterations):\n",
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@ -1023,7 +994,7 @@
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1050,12 +1021,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# not in the book\n",
|
||||
"\n",
|
||||
"# not in the book – this is the same code as earlier\n",
|
||||
"np.random.seed(42)\n",
|
||||
"m = 100\n",
|
||||
"X = 6 * np.random.rand(m, 1) - 3\n",
|
||||
|
|
@ -1066,7 +1036,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1081,7 +1051,7 @@
|
|||
"sgd_reg = SGDRegressor(penalty=None, eta0=0.002, random_state=42)\n",
|
||||
"n_epochs = 500\n",
|
||||
"best_valid_rmse = float('inf')\n",
|
||||
"train_errors, val_errors = [], [] # not in the book, it's for the figure below\n",
|
||||
"train_errors, val_errors = [], [] # not in the book – it's for the figure below\n",
|
||||
"\n",
|
||||
"for epoch in range(n_epochs):\n",
|
||||
" sgd_reg.partial_fit(X_train_prep, y_train)\n",
|
||||
|
|
@ -1091,13 +1061,13 @@
|
|||
" best_valid_rmse = val_error\n",
|
||||
" best_model = deepcopy(sgd_reg)\n",
|
||||
"\n",
|
||||
" # not in the book, we evaluate the train error and save it for the figure\n",
|
||||
" # not in the book – we evaluate the train error and save it for the figure\n",
|
||||
" y_train_predict = sgd_reg.predict(X_train_prep)\n",
|
||||
" train_error = mean_squared_error(y_train, y_train_predict, squared=False)\n",
|
||||
" val_errors.append(val_error)\n",
|
||||
" train_errors.append(train_error)\n",
|
||||
"\n",
|
||||
"# not in the book, this code just creates the figure below\n",
|
||||
"# not in the book – this section generates and saves Figure 4–20\n",
|
||||
"best_epoch = np.argmin(val_errors)\n",
|
||||
"plt.figure(figsize=(6, 4))\n",
|
||||
"plt.annotate('Best model',\n",
|
||||
|
|
@ -1134,11 +1104,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# not in the book\n",
|
||||
"# not in the book – generates and saves Figure 4–21\n",
|
||||
"\n",
|
||||
"lim = 6\n",
|
||||
"t = np.linspace(-lim, lim, 100)\n",
|
||||
|
|
@ -1168,7 +1138,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1180,18 +1150,16 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# not in the book\n",
|
||||
"\n",
|
||||
"print(iris.DESCR)"
|
||||
"print(iris.DESCR) # not in the book – it's a bit too long"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1200,7 +1168,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1209,7 +1177,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1218,7 +1186,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1235,7 +1203,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1243,14 +1211,14 @@
|
|||
"y_proba = log_reg.predict_proba(X_new)\n",
|
||||
"decision_boundary = X_new[y_proba[:, 1] >= 0.5][0, 0]\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(8, 3)) # not in the book\n",
|
||||
"plt.figure(figsize=(8, 3)) # not in the book – not needed, just formatting\n",
|
||||
"plt.plot(X_new, y_proba[:, 0], \"b--\", linewidth=2,\n",
|
||||
" label=\"Not Iris virginica proba\")\n",
|
||||
"plt.plot(X_new, y_proba[:, 1], \"g-\", linewidth=2, label=\"Iris virginica proba\")\n",
|
||||
"plt.plot([decision_boundary, decision_boundary], [0, 1], \"k:\", linewidth=2,\n",
|
||||
" label=\"Decision boundary\")\n",
|
||||
"\n",
|
||||
"# not in the book: beautifies the figure\n",
|
||||
"# not in the book – this section beautifies and saves Figure 4–21\n",
|
||||
"plt.arrow(x=decision_boundary, y=0.08, dx=-0.3, dy=0,\n",
|
||||
" head_width=0.05, head_length=0.1, fc=\"b\", ec=\"b\")\n",
|
||||
"plt.arrow(x=decision_boundary, y=0.92, dx=0.3, dy=0,\n",
|
||||
|
|
@ -1269,7 +1237,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1278,7 +1246,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1287,11 +1255,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# not in the book\n",
|
||||
"# not in the book – this cell generates and saves Figure 4–22\n",
|
||||
"\n",
|
||||
"X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
|
||||
"y = iris.target_names[iris.target] == 'virginica'\n",
|
||||
|
|
@ -1337,7 +1305,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1351,7 +1319,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"execution_count": 58,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
|
|
@ -1362,7 +1330,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"execution_count": 59,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
|
|
@ -1373,10 +1341,12 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# not in the book – this cell generates and saves Figure 4–23\n",
|
||||
"\n",
|
||||
"from matplotlib.colors import ListedColormap\n",
|
||||
"\n",
|
||||
"custom_cmap = ListedColormap([\"#fafab0\", \"#9898ff\", \"#a0faa0\"])\n",
|
||||
|
|
@ -1446,7 +1416,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1463,7 +1433,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1479,7 +1449,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1511,7 +1481,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1528,7 +1498,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1537,7 +1507,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1553,7 +1523,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 71,
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1571,7 +1541,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 72,
|
||||
"execution_count": 68,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1593,7 +1563,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 73,
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1612,7 +1582,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 74,
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1640,7 +1610,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"execution_count": 71,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1673,7 +1643,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"execution_count": 72,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1689,7 +1659,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 77,
|
||||
"execution_count": 73,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1710,7 +1680,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 78,
|
||||
"execution_count": 74,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1747,7 +1717,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 79,
|
||||
"execution_count": 75,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1775,7 +1745,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 80,
|
||||
"execution_count": 76,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1812,7 +1782,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 81,
|
||||
"execution_count": 77,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1840,7 +1810,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 82,
|
||||
"execution_count": 78,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1884,7 +1854,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 83,
|
||||
"execution_count": 79,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
|
|||
Loading…
Reference in New Issue