update week 35
This commit is contained in:
Morten Hjorth-Jensen
@@ -374,7 +374,7 @@ MathJax.Hub.Config({
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Sep 2, 2021</h4></center> <!-- date -->
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<center><h4>Sep 3, 2021</h4></center> <!-- date -->
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<br>
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<p>
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@@ -148,7 +148,7 @@ MathJax.Hub.Config({
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p> <br>
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<center><h4>Sep 2, 2021</h4></center> <!-- date -->
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<center><h4>Sep 3, 2021</h4></center> <!-- date -->
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<br>
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<p>
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@@ -162,9 +162,9 @@ MathJax.Hub.Config({
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<h2 id="plans-for-week-35-august-30-september-3">Plans for week 35, August 30 -September 3 </h2>
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<ul>
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<p><li> Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression</li>
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<p><li> Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage" target="_blank">Video of lecture Thursday</a>.</li>
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<p><li> Friday: Analysis of Ridge and Lasso Regression</li>
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<p><li> Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition</li>
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</ul>
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</section>
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@@ -1972,7 +1972,7 @@ If you wish to include the zero singular values, you will need to resize the mat
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<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember11.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture from 2020</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember11.pdf" target="_blank">handwritten notes</a>
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<p>
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More material will be added here, see handwritten notes also.
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More material will be added here, see handwritten notes also. Note that this material will be cleaned up after the lecture of Friday September 3. See the handwritten notes from Friday's lecture at <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021</tt></a>.
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</section>
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@@ -300,7 +300,7 @@ MathJax.Hub.Config({
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Sep 2, 2021</h4></center> <!-- date -->
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<center><h4>Sep 3, 2021</h4></center> <!-- date -->
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<br>
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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@@ -308,9 +308,9 @@ MathJax.Hub.Config({
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<h2 id="plans-for-week-35-august-30-september-3">Plans for week 35, August 30 -September 3 </h2>
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<ul>
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<li> Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression</li>
|
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<li> Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition</li>
|
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<li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage" target="_blank">Video of lecture Thursday</a>.</li>
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<li> Friday: Analysis of Ridge and Lasso Regression</li>
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<li> Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition</li>
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</ul>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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@@ -2032,7 +2032,7 @@ If you wish to include the zero singular values, you will need to resize the mat
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<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember11.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture from 2020</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember11.pdf" target="_blank">handwritten notes</a>
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<p>
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More material will be added here, see handwritten notes also.
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More material will be added here, see handwritten notes also. Note that this material will be cleaned up after the lecture of Friday September 3. See the handwritten notes from Friday's lecture at <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021</tt></a>.
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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@@ -305,7 +305,7 @@ MathJax.Hub.Config({
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Sep 2, 2021</h4></center> <!-- date -->
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<center><h4>Sep 3, 2021</h4></center> <!-- date -->
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<br>
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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@@ -313,9 +313,9 @@ MathJax.Hub.Config({
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<h2 id="plans-for-week-35-august-30-september-3">Plans for week 35, August 30 -September 3 </h2>
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<ul>
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<li> Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression</li>
|
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<li> Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition</li>
|
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<li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage" target="_blank">Video of lecture Thursday</a>.</li>
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<li> Friday: Analysis of Ridge and Lasso Regression</li>
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<li> Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition</li>
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</ul>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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@@ -2037,7 +2037,7 @@ If you wish to include the zero singular values, you will need to resize the mat
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<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember11.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture from 2020</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember11.pdf" target="_blank">handwritten notes</a>
|
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<p>
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More material will be added here, see handwritten notes also.
|
||||
More material will be added here, see handwritten notes also. Note that this material will be cleaned up after the lecture of Friday September 3. See the handwritten notes from Friday's lecture at <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021</tt></a>.
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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-569
@@ -10,7 +10,7 @@
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"<!-- Author: --> \n",
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"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
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"\n",
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"Date: **Sep 2, 2021**\n",
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"Date: **Sep 3, 2021**\n",
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"\n",
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"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
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"\n",
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@@ -19,11 +19,11 @@
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"\n",
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"## Plans for week 35, August 30 -September 3\n",
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"\n",
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"* Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression\n",
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"* Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition\n",
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"\n",
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"* [Video of lecture Thursday](https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage).\n",
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"\n",
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"* Friday: Analysis of Ridge and Lasso Regression\n",
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"* Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition\n",
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"\n",
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"## Thursday September 2\n",
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"\n",
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@@ -400,7 +400,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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@@ -943,7 +946,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# matrix inversion to find beta\n",
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@@ -962,7 +968,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
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@@ -979,7 +988,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"Masses['Eapprox'] = ytilde\n",
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@@ -1009,7 +1021,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"def R2(y_data, y_model):\n",
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@@ -1026,7 +1041,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"print(R2(Energies,ytilde))"
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@@ -1042,7 +1060,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"def MSE(y_data,y_model):\n",
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@@ -1062,7 +1083,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"def RelativeError(y_data,y_model):\n",
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@@ -1097,7 +1121,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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@@ -1150,7 +1177,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# equivalently in numpy\n",
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@@ -1222,7 +1252,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@@ -1242,7 +1275,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.datasets import load_boston\n",
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@@ -1264,7 +1300,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
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@@ -1282,7 +1321,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# check for missing values in all the columns\n",
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@@ -1299,7 +1341,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# set the size of the figure\n",
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# compute the pair wise correlation for all columns \n",
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@@ -1340,7 +1388,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(20, 5))\n",
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@@ -1368,7 +1419,10 @@
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n",
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LinearRegression\n",
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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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"metadata": {
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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# plotting the y_test vs y_pred\n",
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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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{
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"data": {
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" <th>0</th>\n",
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" <td>-1.749765</td>\n",
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" <td>0.342680</td>\n",
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" <td>1.153036</td>\n",
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" <td>-0.252436</td>\n",
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" <td>0.981321</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0.514219</td>\n",
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" <td>0.221180</td>\n",
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" <td>-1.070043</td>\n",
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" <td>-0.189496</td>\n",
|
||||
" <td>0.255001</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>-0.458027</td>\n",
|
||||
" <td>0.435163</td>\n",
|
||||
" <td>-0.583595</td>\n",
|
||||
" <td>0.816847</td>\n",
|
||||
" <td>0.672721</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>-0.104411</td>\n",
|
||||
" <td>-0.531280</td>\n",
|
||||
" <td>1.029733</td>\n",
|
||||
" <td>-0.438136</td>\n",
|
||||
" <td>-1.118318</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>1.618982</td>\n",
|
||||
" <td>1.541605</td>\n",
|
||||
" <td>-0.251879</td>\n",
|
||||
" <td>-0.842436</td>\n",
|
||||
" <td>0.184519</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>0.937082</td>\n",
|
||||
" <td>0.731000</td>\n",
|
||||
" <td>1.361556</td>\n",
|
||||
" <td>-0.326238</td>\n",
|
||||
" <td>0.055676</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>0.222400</td>\n",
|
||||
" <td>-1.443217</td>\n",
|
||||
" <td>-0.756352</td>\n",
|
||||
" <td>0.816454</td>\n",
|
||||
" <td>0.750445</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>-0.455947</td>\n",
|
||||
" <td>1.189622</td>\n",
|
||||
" <td>-1.690617</td>\n",
|
||||
" <td>-1.356399</td>\n",
|
||||
" <td>-1.232435</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>-0.544439</td>\n",
|
||||
" <td>-0.668172</td>\n",
|
||||
" <td>0.007315</td>\n",
|
||||
" <td>-0.612939</td>\n",
|
||||
" <td>1.299748</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>-1.733096</td>\n",
|
||||
" <td>-0.983310</td>\n",
|
||||
" <td>0.357508</td>\n",
|
||||
" <td>-1.613579</td>\n",
|
||||
" <td>1.470714</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 0 1 2 3 4\n",
|
||||
"0 -1.749765 0.342680 1.153036 -0.252436 0.981321\n",
|
||||
"1 0.514219 0.221180 -1.070043 -0.189496 0.255001\n",
|
||||
"2 -0.458027 0.435163 -0.583595 0.816847 0.672721\n",
|
||||
"3 -0.104411 -0.531280 1.029733 -0.438136 -1.118318\n",
|
||||
"4 1.618982 1.541605 -0.251879 -0.842436 0.184519\n",
|
||||
"5 0.937082 0.731000 1.361556 -0.326238 0.055676\n",
|
||||
"6 0.222400 -1.443217 -0.756352 0.816454 0.750445\n",
|
||||
"7 -0.455947 1.189622 -1.690617 -1.356399 -1.232435\n",
|
||||
"8 -0.544439 -0.668172 0.007315 -0.612939 1.299748\n",
|
||||
"9 -1.733096 -0.983310 0.357508 -1.613579 1.470714"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0 -0.175300\n",
|
||||
"1 0.083527\n",
|
||||
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|
||||
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|
||||
"4 0.331939\n",
|
||||
"dtype: float64\n",
|
||||
"0 1.069584\n",
|
||||
"1 0.965548\n",
|
||||
"2 1.018232\n",
|
||||
"3 0.793167\n",
|
||||
"4 0.918992\n",
|
||||
"dtype: float64\n"
|
||||
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|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
" <th>2</th>\n",
|
||||
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|
||||
" <th>4</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>-1.574465</td>\n",
|
||||
" <td>0.259153</td>\n",
|
||||
" <td>1.197370</td>\n",
|
||||
" <td>0.147400</td>\n",
|
||||
" <td>0.649382</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0.689519</td>\n",
|
||||
" <td>0.137652</td>\n",
|
||||
" <td>-1.025709</td>\n",
|
||||
" <td>0.210340</td>\n",
|
||||
" <td>-0.076938</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>-0.282727</td>\n",
|
||||
" <td>0.351636</td>\n",
|
||||
" <td>-0.539261</td>\n",
|
||||
" <td>1.216683</td>\n",
|
||||
" <td>0.340782</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.070889</td>\n",
|
||||
" <td>-0.614808</td>\n",
|
||||
" <td>1.074067</td>\n",
|
||||
" <td>-0.038300</td>\n",
|
||||
" <td>-1.450257</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>1.794282</td>\n",
|
||||
" <td>1.458078</td>\n",
|
||||
" <td>-0.207545</td>\n",
|
||||
" <td>-0.442600</td>\n",
|
||||
" <td>-0.147420</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>1.112383</td>\n",
|
||||
" <td>0.647473</td>\n",
|
||||
" <td>1.405890</td>\n",
|
||||
" <td>0.073598</td>\n",
|
||||
" <td>-0.276263</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>0.397700</td>\n",
|
||||
" <td>-1.526744</td>\n",
|
||||
" <td>-0.712018</td>\n",
|
||||
" <td>1.216290</td>\n",
|
||||
" <td>0.418506</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>-0.280647</td>\n",
|
||||
" <td>1.106095</td>\n",
|
||||
" <td>-1.646283</td>\n",
|
||||
" <td>-0.956563</td>\n",
|
||||
" <td>-1.564374</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>-0.369139</td>\n",
|
||||
" <td>-0.751699</td>\n",
|
||||
" <td>0.051649</td>\n",
|
||||
" <td>-0.213103</td>\n",
|
||||
" <td>0.967809</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>-1.557795</td>\n",
|
||||
" <td>-1.066837</td>\n",
|
||||
" <td>0.401842</td>\n",
|
||||
" <td>-1.213743</td>\n",
|
||||
" <td>1.138775</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 0 1 2 3 4\n",
|
||||
"0 -1.574465 0.259153 1.197370 0.147400 0.649382\n",
|
||||
"1 0.689519 0.137652 -1.025709 0.210340 -0.076938\n",
|
||||
"2 -0.282727 0.351636 -0.539261 1.216683 0.340782\n",
|
||||
"3 0.070889 -0.614808 1.074067 -0.038300 -1.450257\n",
|
||||
"4 1.794282 1.458078 -0.207545 -0.442600 -0.147420\n",
|
||||
"5 1.112383 0.647473 1.405890 0.073598 -0.276263\n",
|
||||
"6 0.397700 -1.526744 -0.712018 1.216290 0.418506\n",
|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
},
|
||||
"metadata": {},
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
" <th>4</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
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||||
" <tr>\n",
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||||
" <th>0</th>\n",
|
||||
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||||
" <td>0.0</td>\n",
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||||
" <td>0.0</td>\n",
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||||
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||||
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||||
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||||
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||||
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|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 0 1 2 3 4\n",
|
||||
"0 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"1 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"2 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"3 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"4 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"5 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"6 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"7 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"8 0.0 0.0 0.0 0.0 0.0\n",
|
||||
"9 0.0 0.0 0.0 0.0 0.0"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sklearn.linear_model as skl\n",
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
@@ -2055,7 +1712,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"np.random.seed()\n",
|
||||
@@ -2078,7 +1738,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
@@ -2129,7 +1792,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Common imports\n",
|
||||
@@ -2692,7 +2358,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
@@ -2762,7 +2431,7 @@
|
||||
"\n",
|
||||
"[Video of Lecture from 2020](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember11.mp4?vrtx=view-as-webpage) and [handwritten notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember11.pdf)\n",
|
||||
"\n",
|
||||
"More material will be added here, see handwritten notes also.\n",
|
||||
"More material will be added here, see handwritten notes also. Note that this material will be cleaned up after the lecture of Friday September 3. See the handwritten notes from Friday's lecture at <https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021>.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Ridge and LASSO Regression\n",
|
||||
@@ -3393,20 +3062,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"-0.22501987939529708\n",
|
||||
"3.4265364730355325\n",
|
||||
"[[1.06251574 3.06513214]\n",
|
||||
" [3.06513214 9.90295583]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Importing various packages\n",
|
||||
"import numpy as np\n",
|
||||
@@ -3435,20 +3096,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.080263294546264\n",
|
||||
"2.011002230765845\n",
|
||||
"[[1. 0.7136592]\n",
|
||||
" [0.7136592 1. ]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"n = 100\n",
|
||||
@@ -3490,40 +3143,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[-0.31787985 -1.14338084]\n",
|
||||
" [ 0.04529807 -0.18207237]\n",
|
||||
" [ 1.11176315 3.45031813]\n",
|
||||
" [-1.91097714 -5.2907413 ]\n",
|
||||
" [-0.19918861 1.18654759]\n",
|
||||
" [ 0.84454053 2.34712647]\n",
|
||||
" [ 0.82299467 1.93582947]\n",
|
||||
" [-1.64434822 -5.13289015]\n",
|
||||
" [ 1.10923753 3.75337971]\n",
|
||||
" [ 0.13855987 -0.92411672]]\n",
|
||||
" 0 1\n",
|
||||
"0 -0.317880 -1.143381\n",
|
||||
"1 0.045298 -0.182072\n",
|
||||
"2 1.111763 3.450318\n",
|
||||
"3 -1.910977 -5.290741\n",
|
||||
"4 -0.199189 1.186548\n",
|
||||
"5 0.844541 2.347126\n",
|
||||
"6 0.822995 1.935829\n",
|
||||
"7 -1.644348 -5.132890\n",
|
||||
"8 1.109238 3.753380\n",
|
||||
"9 0.138560 -0.924117\n",
|
||||
" 0 1\n",
|
||||
"0 1.000000 0.968568\n",
|
||||
"1 0.968568 1.000000\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
@@ -3551,49 +3176,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0 1 2 3 4 5 6 7 \\\n",
|
||||
"0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
|
||||
"1 0.0 0.063613 0.068251 0.071169 0.070546 0.069425 0.067581 0.065899 \n",
|
||||
"2 0.0 0.068251 0.073991 0.077286 0.077098 0.076290 0.073977 0.072493 \n",
|
||||
"3 0.0 0.071169 0.077286 0.083456 0.083469 0.082851 0.081789 0.080312 \n",
|
||||
"4 0.0 0.070546 0.077098 0.083469 0.083869 0.083597 0.082364 0.081188 \n",
|
||||
"5 0.0 0.069425 0.076290 0.082851 0.083597 0.083643 0.082304 0.081413 \n",
|
||||
"6 0.0 0.067581 0.073977 0.081789 0.082364 0.082304 0.082005 0.080988 \n",
|
||||
"7 0.0 0.065899 0.072493 0.080312 0.081188 0.081413 0.080988 0.080245 \n",
|
||||
"8 0.0 0.064203 0.070945 0.078766 0.079908 0.080389 0.079865 0.079371 \n",
|
||||
"9 0.0 0.062545 0.069397 0.077214 0.078591 0.079301 0.078699 0.078431 \n",
|
||||
"10 0.0 0.061809 0.068087 0.076517 0.077496 0.077870 0.078063 0.077474 \n",
|
||||
"11 0.0 0.060077 0.066459 0.074794 0.076005 0.076605 0.076675 0.076314 \n",
|
||||
"12 0.0 0.058442 0.064904 0.073149 0.074564 0.075366 0.075332 0.075177 \n",
|
||||
"13 0.0 0.056909 0.063432 0.071590 0.073188 0.074169 0.074047 0.074077 \n",
|
||||
"14 0.0 0.055478 0.062046 0.070123 0.071881 0.073022 0.072826 0.073022 \n",
|
||||
"\n",
|
||||
" 8 9 10 11 12 13 14 \n",
|
||||
"0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
|
||||
"1 0.064203 0.062545 0.061809 0.060077 0.058442 0.056909 0.055478 \n",
|
||||
"2 0.070945 0.069397 0.068087 0.066459 0.064904 0.063432 0.062046 \n",
|
||||
"3 0.078766 0.077214 0.076517 0.074794 0.073149 0.071590 0.070123 \n",
|
||||
"4 0.079908 0.078591 0.077496 0.076005 0.074564 0.073188 0.071881 \n",
|
||||
"5 0.080389 0.079301 0.077870 0.076605 0.075366 0.074169 0.073022 \n",
|
||||
"6 0.079865 0.078699 0.078063 0.076675 0.075332 0.074047 0.072826 \n",
|
||||
"7 0.079371 0.078431 0.077474 0.076314 0.075177 0.074077 0.073022 \n",
|
||||
"8 0.078726 0.077994 0.076758 0.075809 0.074861 0.073933 0.073033 \n",
|
||||
"9 0.077994 0.077452 0.075974 0.075216 0.074443 0.073672 0.072915 \n",
|
||||
"10 0.076758 0.075974 0.075342 0.074315 0.073305 0.072325 0.071384 \n",
|
||||
"11 0.075809 0.075216 0.074315 0.073486 0.072655 0.071838 0.071044 \n",
|
||||
"12 0.074861 0.074443 0.073305 0.072655 0.071987 0.071318 0.070658 \n",
|
||||
"13 0.073933 0.073672 0.072325 0.071838 0.071318 0.070783 0.070245 \n",
|
||||
"14 0.073033 0.072915 0.071384 0.071044 0.070658 0.070245 0.069818 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Common imports\n",
|
||||
"import numpy as np\n",
|
||||
@@ -3883,7 +3471,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x = np.random.rand(100)\n",
|
||||
@@ -3905,7 +3496,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
@@ -4100,7 +3694,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||||
@@ -4218,7 +3815,10 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def FrankeFunction(x,y):\n",
|
||||
@@ -4276,25 +3876,7 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.5"
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@@ -6,10 +6,10 @@ DATE: today
|
||||
!split
|
||||
===== Plans for week 35, August 30 -September 3 =====
|
||||
|
||||
* Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression
|
||||
* Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition
|
||||
* "Video of lecture Thursday":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage".
|
||||
|
||||
* Friday: Analysis of Ridge and Lasso Regression
|
||||
* Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition
|
||||
|
||||
|
||||
|
||||
@@ -1544,7 +1544,7 @@ If you wish to include the zero singular values, you will need to resize the mat
|
||||
|
||||
"Video of Lecture from 2020":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember11.mp4?vrtx=view-as-webpage" and "handwritten notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember11.pdf"
|
||||
|
||||
More material will be added here, see handwritten notes also.
|
||||
More material will be added here, see handwritten notes also. Note that this material will be cleaned up after the lecture of Friday September 3. See the handwritten notes from Friday's lecture at URL:"https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021".
|
||||
|
||||
|
||||
!split
|
||||
|
||||
Reference in New Issue
Block a user