Pushing the docs to dev/ for branch: master, commit 3c1873550317bbbc63e982cccbd0afaae9cc5a66

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
     }, 
     {
       "source": [
-        "\n# Logit function\n\n\nShow in the plot is how the logistic regression would, in this\nsynthetic dataset, classify values as either 0 or 1,\ni.e. class one or two, using the logit-curve.\n\n\n"
+        "\n# Logistic function\n\n\nShown in the plot is how the logistic regression would, in this\nsynthetic dataset, classify values as either 0 or 1,\ni.e. class one or two, using the logistic curve.\n\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}
@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\n\n# Code source: Gael Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\n\n# this is our test set, it's just a straight line with some\n# Gaussian noise\nxmin, xmax = -5, 5\nn_samples = 100\nnp.random.seed(0)\nX = np.random.normal(size=n_samples)\ny = (X > 0).astype(np.float)\nX[X > 0] *= 4\nX += .3 * np.random.normal(size=n_samples)\n\nX = X[:, np.newaxis]\n# run the classifier\nclf = linear_model.LogisticRegression(C=1e5)\nclf.fit(X, y)\n\n# and plot the result\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.scatter(X.ravel(), y, color='black', zorder=20)\nX_test = np.linspace(-5, 10, 300)\n\n\ndef model(x):\n    return 1 / (1 + np.exp(-x))\nloss = model(X_test * clf.coef_ + clf.intercept_).ravel()\nplt.plot(X_test, loss, color='blue', linewidth=3)\n\nols = linear_model.LinearRegression()\nols.fit(X, y)\nplt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)\nplt.axhline(.5, color='.5')\n\nplt.ylabel('y')\nplt.xlabel('X')\nplt.xticks(())\nplt.yticks(())\nplt.ylim(-.25, 1.25)\nplt.xlim(-4, 10)\n\nplt.show()"
+        "print(__doc__)\n\n\n# Code source: Gael Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\n\n# this is our test set, it's just a straight line with some\n# Gaussian noise\nxmin, xmax = -5, 5\nn_samples = 100\nnp.random.seed(0)\nX = np.random.normal(size=n_samples)\ny = (X > 0).astype(np.float)\nX[X > 0] *= 4\nX += .3 * np.random.normal(size=n_samples)\n\nX = X[:, np.newaxis]\n# run the classifier\nclf = linear_model.LogisticRegression(C=1e5)\nclf.fit(X, y)\n\n# and plot the result\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.scatter(X.ravel(), y, color='black', zorder=20)\nX_test = np.linspace(-5, 10, 300)\n\n\ndef model(x):\n    return 1 / (1 + np.exp(-x))\nloss = model(X_test * clf.coef_ + clf.intercept_).ravel()\nplt.plot(X_test, loss, color='red', linewidth=3)\n\nols = linear_model.LinearRegression()\nols.fit(X, y)\nplt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)\nplt.axhline(.5, color='.5')\n\nplt.ylabel('y')\nplt.xlabel('X')\nplt.xticks(range(-5, 10))\nplt.yticks([0, 0.5, 1])\nplt.ylim(-.25, 1.25)\nplt.xlim(-4, 10)\nplt.legend(('Logistic Regression Model', 'Linear Regression Model'),\n           loc=\"lower right\", fontsize='small')\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -4,12 +4,12 @@
 
 """
 =========================================================
-Logit function
+Logistic function
 =========================================================
 
-Show in the plot is how the logistic regression would, in this
+Shown in the plot is how the logistic regression would, in this
 synthetic dataset, classify values as either 0 or 1,
-i.e. class one or two, using the logit-curve.
+i.e. class one or two, using the logistic curve.
 
 """
 print(__doc__)
@@ -48,7 +48,7 @@
 def model(x):
     return 1 / (1 + np.exp(-x))
 loss = model(X_test * clf.coef_ + clf.intercept_).ravel()
-plt.plot(X_test, loss, color='blue', linewidth=3)
+plt.plot(X_test, loss, color='red', linewidth=3)
 
 ols = linear_model.LinearRegression()
 ols.fit(X, y)
@@ -57,9 +57,10 @@ def model(x):
 
 plt.ylabel('y')
 plt.xlabel('X')
-plt.xticks(())
-plt.yticks(())
+plt.xticks(range(-5, 10))
+plt.yticks([0, 0.5, 1])
 plt.ylim(-.25, 1.25)
 plt.xlim(-4, 10)
-
+plt.legend(('Logistic Regression Model', 'Linear Regression Model'),
+           loc="lower right", fontsize='small')
 plt.show()