Merge branch 'master' of https://github.com/CompPhysics/MachineLearning
This commit is contained in:
Morten Hjorth-Jensen
@@ -235,37 +235,37 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics)
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- Lab Wednesday: Introduction to software and repetition of Python Programming
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- Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression
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- Lecture Friday: Basics of Linear Regression
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- Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html
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- Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
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### Week 35 August 30-September 3
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- Lab Wednesday:
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- Lecture Thursday: Linear Regression, from ordinary linear regression to Ridge and Lasso regression, linear algebra analysis, examples and discussions of codes
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- Lecture Friday: Linear Regression, Linear algebra and Ridge and Lasso Regression, linear algebra analysis, examples and discussions of codes
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- Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1
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- Reading recommendations: See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1
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### Week 36 September 6-10
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- Lab Wednesday:
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- Lecture Thursday: Statistical interpretation of Linear Regression
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- Lecture Friday: Bias-Variance tradeoff
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- Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2
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- Lecture Thursday: Ridge and Lasso regression and the SVD. Statistical interpretation of Linear Regression
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- Lecture Friday: Further interpretations of Linear regression.
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- Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2
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- Chapter
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### Week 37 September 13-17
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- Lab Wednesday:
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- Lecture Thursday: Resampling methods, cross-validation and Bootstrap
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- Lecture Friday: More on Resampling methods and summary of linear regression
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- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Chapter
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### Week 38 September 20-24
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- Lab Wednesday:
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- Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories
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- Lecture Friday: Logistic Regression and gradient optimization
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- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Chapter
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### Week 39 September 27- October 1
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- Lab Wednesday:
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- Lecture Thursday: Gradient Optimization methods
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- Lecture Friday: Deep Learning and Neural Networks
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- Reading recommendations: See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Chapter
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### Week 40 October 4-8
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- Lab Wednesday:
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+178
-147
@@ -402,10 +402,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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@@ -956,10 +953,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# matrix inversion to find beta\n",
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@@ -978,10 +972,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -998,10 +989,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"Masses['Eapprox'] = ytilde\n",
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@@ -1031,10 +1019,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1051,10 +1036,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"print(R2(Energies,ytilde))"
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@@ -1070,10 +1052,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1093,10 +1072,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1131,10 +1107,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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@@ -1187,10 +1160,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# equivalently in numpy\n",
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@@ -1262,10 +1232,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@@ -1285,10 +1252,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1310,10 +1274,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1331,10 +1292,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1351,10 +1309,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1375,10 +1330,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1398,10 +1350,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(20, 5))\n",
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@@ -1429,10 +1378,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1449,10 +1395,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1476,10 +1419,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1518,10 +1458,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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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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@@ -1649,10 +1586,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import sklearn.linear_model as skl\n",
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@@ -1722,10 +1656,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed()\n",
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@@ -1748,10 +1679,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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@@ -1802,10 +1730,7 @@
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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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"collapsed": false,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Common imports\n",
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@@ -2379,12 +2304,31 @@
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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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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 1. -1.]\n",
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" [ 1. -1.]]\n",
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"test U\n",
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"[[0. 0.]\n",
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" [0. 0.]]\n",
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"test VT\n",
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"[[0. 0.]\n",
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" [0. 0.]]\n",
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"[[-0.70710678 -0.70710678]\n",
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" [-0.70710678 0.70710678]]\n",
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"[2.00000000e+00 3.35470445e-17]\n",
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"[[-0.70710678 0.70710678]\n",
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" [ 0.70710678 0.70710678]]\n",
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"[[-3.33066907e-16 4.44089210e-16]\n",
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" [ 0.00000000e+00 2.22044605e-16]]\n"
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]
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}
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],
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"source": [
|
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"import numpy as np\n",
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"# SVD inversion\n",
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@@ -3182,12 +3126,20 @@
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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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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"-0.08839767027751376\n",
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"3.8294285924714866\n",
|
||||
"[[0.92932998 2.63805954]\n",
|
||||
" [2.63805954 8.61477117]]\n"
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]
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}
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],
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"source": [
|
||||
"# Importing various packages\n",
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"import numpy as np\n",
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@@ -3216,12 +3168,20 @@
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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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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.0850713352585812\n",
|
||||
"1.5846946541436007\n",
|
||||
"[[1. 0.63837291]\n",
|
||||
" [0.63837291 1. ]]\n"
|
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]
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}
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],
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"source": [
|
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"import numpy as np\n",
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"n = 100\n",
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@@ -3263,12 +3223,40 @@
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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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"collapsed": false,
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"editable": true
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},
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"outputs": [],
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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||||
"[[ 1.09669785 3.23795259]\n",
|
||||
" [-0.22166894 -1.30593687]\n",
|
||||
" [ 0.10192631 0.04245426]\n",
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" [ 0.82011099 2.55486566]\n",
|
||||
" [ 0.32105408 0.96706068]\n",
|
||||
" [ 0.60361795 1.47703672]\n",
|
||||
" [-1.87875598 -5.24764141]\n",
|
||||
" [ 0.03658513 0.37688017]\n",
|
||||
" [-0.57033315 -1.70980756]\n",
|
||||
" [-0.30923424 -0.39286425]]\n",
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" 0 1\n",
|
||||
"0 1.096698 3.237953\n",
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||||
"1 -0.221669 -1.305937\n",
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"2 0.101926 0.042454\n",
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"3 0.820111 2.554866\n",
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||||
"4 0.321054 0.967061\n",
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||||
"5 0.603618 1.477037\n",
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"6 -1.878756 -5.247641\n",
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"7 0.036585 0.376880\n",
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||||
"8 -0.570333 -1.709808\n",
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||||
"9 -0.309234 -0.392864\n",
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||||
" 0 1\n",
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"0 1.000000 0.990742\n",
|
||||
"1 0.990742 1.000000\n"
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]
|
||||
}
|
||||
],
|
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"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
@@ -3297,12 +3285,49 @@
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": 5,
|
||||
"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 0.079955 0.083252 0.080480 0.081776 0.083055 0.071202 0.072287 \n",
|
||||
"2 0.0 0.083252 0.087624 0.083966 0.085810 0.087616 0.074576 0.076060 \n",
|
||||
"3 0.0 0.080480 0.083966 0.085125 0.086868 0.088629 0.077734 0.079241 \n",
|
||||
"4 0.0 0.081776 0.085810 0.086868 0.089002 0.091153 0.079679 0.081504 \n",
|
||||
"5 0.0 0.083055 0.087616 0.088629 0.091153 0.093695 0.081663 0.083808 \n",
|
||||
"6 0.0 0.071202 0.074576 0.077734 0.079679 0.081663 0.072591 0.074289 \n",
|
||||
"7 0.0 0.072287 0.076060 0.079241 0.081504 0.083808 0.074289 0.076255 \n",
|
||||
"8 0.0 0.073485 0.077660 0.080883 0.083467 0.086097 0.076120 0.078358 \n",
|
||||
"9 0.0 0.074811 0.079394 0.082672 0.085583 0.088544 0.078096 0.080610 \n",
|
||||
"10 0.0 0.061639 0.064871 0.068789 0.070813 0.072887 0.065314 0.067084 \n",
|
||||
"11 0.0 0.062699 0.066259 0.070225 0.072518 0.074865 0.066904 0.068904 \n",
|
||||
"12 0.0 0.063878 0.067775 0.071800 0.074368 0.076991 0.068629 0.070864 \n",
|
||||
"13 0.0 0.065183 0.069422 0.073519 0.076366 0.079274 0.070494 0.072970 \n",
|
||||
"14 0.0 0.066615 0.071207 0.075386 0.078520 0.081718 0.072505 0.075226 \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 0.073485 0.074811 0.061639 0.062699 0.063878 0.065183 0.066615 \n",
|
||||
"2 0.077660 0.079394 0.064871 0.066259 0.067775 0.069422 0.071207 \n",
|
||||
"3 0.080883 0.082672 0.068789 0.070225 0.071800 0.073519 0.075386 \n",
|
||||
"4 0.083467 0.085583 0.070813 0.072518 0.074368 0.076366 0.078520 \n",
|
||||
"5 0.086097 0.088544 0.072887 0.074865 0.076991 0.079274 0.081718 \n",
|
||||
"6 0.076120 0.078096 0.065314 0.066904 0.068629 0.070494 0.072505 \n",
|
||||
"7 0.078358 0.080610 0.067084 0.068904 0.070864 0.072970 0.075226 \n",
|
||||
"8 0.080737 0.083272 0.068979 0.071034 0.073233 0.075583 0.078091 \n",
|
||||
"9 0.083272 0.086096 0.071010 0.073303 0.075747 0.078348 0.081114 \n",
|
||||
"10 0.068979 0.071010 0.059527 0.061166 0.062930 0.064825 0.066855 \n",
|
||||
"11 0.071034 0.073303 0.061166 0.063004 0.064972 0.067075 0.069319 \n",
|
||||
"12 0.073233 0.075747 0.062930 0.064972 0.067148 0.069465 0.071929 \n",
|
||||
"13 0.075583 0.078348 0.064825 0.067075 0.069465 0.072001 0.074691 \n",
|
||||
"14 0.078091 0.081114 0.066855 0.069319 0.071929 0.074691 0.077614 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Common imports\n",
|
||||
"import numpy as np\n",
|
||||
@@ -4086,10 +4111,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x = np.random.rand(100)\n",
|
||||
@@ -4111,10 +4133,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
@@ -4309,10 +4328,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||||
@@ -4430,10 +4446,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def FrankeFunction(x,y):\n",
|
||||
@@ -4491,7 +4504,25 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {},
|
||||
"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": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user