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Commit d2072ce

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Minor fixes
1 parent ff6a710 commit d2072ce

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1 file changed

+36
-29
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1 file changed

+36
-29
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‎12141666_12166804_lab1.ipynb‎

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@@ -21,7 +21,9 @@
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"NAME = \"IOANNIS GATOPOULOS\"\n",
@@ -63,6 +65,7 @@
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@@ -293,6 +299,7 @@
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@@ -325,6 +332,7 @@
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"cell_type": "code",
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"checksum": "dc76736fba956e5d9cc51a318c2507c3",
@@ -455,27 +465,11 @@
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" return w_ml, Phi"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# for m in range(0, 9, 2):\n",
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"# w, Phi = fit_polynomial_reg(x, t, m, lamb=0.1)\n",
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"# y = predict(w, x_test)\n",
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"# plt.subplot(2, 3, m/2 + 1)\n",
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"# plt.plot(x_test, np.sin(x_test), 'g', label='sin(x)')\n",
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"# plt.scatter(x, t, c='b', label='Data point')\n",
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"# plt.plot(x_test, y, 'r', label='Regression polyomial')\n",
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"# plt.title(\"Regularized regression with polynomial of order %s\" %m)\n",
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"# plt.legend()"
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]
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},
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{
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"cell_type": "code",
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@@ -526,7 +520,9 @@
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{
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"cell_type": "code",
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},
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"outputs": [],
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"source": [
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"m = 8\n",
@@ -543,7 +539,7 @@
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"x_test = np.linspace(0, 2 * np.pi, 1000)\n",
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"plt.scatter(x_train, t_train, c = 'b', label = 'Data points')\n",
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"plt.plot(x_test, predict(w, x_test), 'g', label = 'Unregularized regression of order %d' %m)\n",
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"plt.plot(x_test, predict(w_reg, x_test), 'r', label = 'Regularized regression of order %d with lamda = %.1f' %(m, lamb))\n",
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"plt.plot(x_test, predict(w_reg, x_test), 'r', label = 'Regularized regression of order %d with lambda = %.1f' %(m, lamb))\n",
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"plt.legend()"
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]
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},
@@ -597,6 +593,7 @@
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"checksum": "65930a94ed4b46300fcf5aef054662a0",
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"cell_type": "code",
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@@ -759,7 +760,10 @@
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"outputs": [],
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"source": [
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"x = np.random.uniform(0, 5, 20)\n",
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"checksum": "4afe3760f68ff7c6b06f18b8e60c71a6",
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"plt.scatter(x_train, t_train, c = 'blue', label = 'Data point')\n",
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"plt.plot(x_test, np.sin(x_test), 'green', label = 'sin(x)')\n",
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"plt.fill_between(x_test, mean + sigma, mean - sigma, color = 'red', alpha = '0.1')\n",
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"plt.fill(np.NaN, np.NaN, 'red', alpha = 0.1, label = 'One std. dev. of predictive distribution')\n",
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"plt.fill(np.NaN, np.NaN, 'red', alpha = 0.1, label = 'Std. dev. of predictive distribution')\n",
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"plt.title(\"Plot of the predictive distribution\")\n",
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"plt.legend()"
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]
@@ -1242,6 +1254,7 @@
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"cell_type": "code",
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"checksum": "a6cbc9e5b0de9f7f9c847b1209275748",
@@ -1337,16 +1350,10 @@
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"source": [
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"It is generally difficult to find the appropriate basis functions that transform the input data so that linear regression on it gives good results. This often requires extensive domain knowledge of the problem to be solved and involves handcrafting the basis functions. Moreover, the number of required basis functions increases exponentially with the dimension of the input data."
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]
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"source": []
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda env:ml1labs]",
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"language": "python",

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