diff --git a/doc/pub/How2ReadData/html/How2ReadData-bs.html b/doc/pub/How2ReadData/html/How2ReadData-bs.html index a47e06046..b3f391ef5 100644 --- a/doc/pub/How2ReadData/html/How2ReadData-bs.html +++ b/doc/pub/How2ReadData/html/How2ReadData-bs.html @@ -55,27 +55,25 @@ Automatically generated HTML file from DocOnce source None, '___sec8'), ('Basic Matrix Features', 2, None, '___sec9'), - ('Basic Matrix Features', 2, None, '___sec10'), - ('Basic Matrix Features', 2, None, '___sec11'), - ('Some famous Matrices', 2, None, '___sec12'), - ('Basic Matrix Features', 2, None, '___sec13'), - ('Numpy and arrays', 2, None, '___sec14'), - ('Matrices in Python', 2, None, '___sec15'), - ('Meet the Pandas', 2, None, '___sec16'), - ('Reading Data and fitting', 2, None, '___sec17'), + ('Some famous Matrices', 3, None, '___sec10'), + ('More Basic Matrix Features', 3, None, '___sec11'), + ('Numpy and arrays', 2, None, '___sec12'), + ('Matrices in Python', 2, None, '___sec13'), + ('Meet the Pandas', 2, None, '___sec14'), + ('Reading Data and fitting', 2, None, '___sec15'), ('Simple linear regression model using _scikit-learn_', 3, None, - '___sec18'), + '___sec16'), ('To our real data: nuclear binding energies. Brief reminder on ' 'masses and binding energies', 3, None, - '___sec19'), - ('Organizing our data', 3, None, '___sec20'), - ('Seeing the wood for the trees', 3, None, '___sec21'), - ('And what about using neural networks?', 3, None, '___sec22'), - ('A first summary', 2, None, '___sec23')]} + '___sec17'), + ('Organizing our data', 3, None, '___sec18'), + ('Seeing the wood for the trees', 3, None, '___sec19'), + ('And what about using neural networks?', 3, None, '___sec20'), + ('A first summary', 2, None, '___sec21')]} end of tocinfo --> @@ -123,20 +121,18 @@ MathJax.Hub.Config({
  • Installing R, C++, cython, Numba etc
  • Numpy examples and Important Matrix and vector handling packages
  • Basic Matrix Features
    • -
  • Basic Matrix Features
    • -
  • Basic Matrix Features
    • -
  • Some famous Matrices
    • -
  • Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Reading Data and fitting
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Reading Data and fitting
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • @@ -170,7 +166,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Aug 13, 2019

    +

    Aug 14, 2019


    @@ -194,7 +190,7 @@ polynomials with random noise added. We will use the Python software package Scikit-Learn and introduce various machine learning algorithms to make fits of the data and predictions. We move thereafter to more interesting -cases such as nuclear binding energies. +cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example). These are examples where we can easily set up the data and then use machine learning algorithms included in for example Scikit-Learn. @@ -204,7 +200,7 @@ These examples will serve us the purpose of getting started. Furthermore, they allow us to catch more than two birds with a stone. They will allow us to bring in some programming specific topics and tools as well as showing the power of various Python -packages for machine learning and statistical data analysis. +libraries for machine learning and statistical data analysis.

    Here, we will mainly focus on two @@ -411,14 +407,14 @@ Here we list several useful Python libraries we strongly recommend (if you use a

    You will also find it convenient to utilize R. We will mainly -use Python during lectures and in various projects and exercises. +use Python during our lectures and in various projects and exercises. Those of you already familiar with R should feel free to continue using R, keeping however an eye on the parallel Python set ups. Similarly, if you are a Python afecionado, feel free to explore R as well. Jupyter/Ipython notebook allows you to run R codes interactively in your -browser. The software library R is tuned to statistically analysis -and allows for an easy usage of the tools we will discuss in these +browser. The software library R is really tailored for statistical data analysis +and allows for an easy usage of the tools and algorithms we will discuss in these lectures.

    @@ -462,7 +458,7 @@ formats, ipython notebooks, latex files, pdf files etc with minimal edits. These

    Numpy examples and Important Matrix and vector handling packages

    -There are several central software packages for linear algebra and eigenvalue problems. Several of the more +There are several central software libraries for linear algebra and eigenvalue problems. Several of the more popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used software package LAPACK, which follows two other popular packages developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here. @@ -493,14 +489,6 @@ $$ 0 & 0 & 0 & 1 \end{bmatrix} $$ - - - - -

    Basic Matrix Features

    -
    -
    -

    The inverse of a matrix is defined by @@ -508,16 +496,6 @@ The inverse of a matrix is defined by $$ \mathbf{A}^{-1} \cdot \mathbf{A} = I $$ -

    -
    - - -

    Basic Matrix Features

    - -

    -

    -
    -

    @@ -542,7 +520,7 @@ $$

    -

    Some famous Matrices

    +

    Some famous Matrices

    -

    Basic Matrix Features

    +

    More Basic Matrix Features

    @@ -576,7 +554,7 @@ For an \( N\times N \) matrix \( \mathbf{A} \) the following properties are all
    -

    Numpy and arrays

    +

    Numpy and arrays

    Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as

    @@ -661,7 +639,7 @@ x = np.l print(x.itemsize)

    -

    Matrices in Python

    +

    Matrices in Python

    Having defined vectors, we are now ready to try out matrices. We can @@ -798,7 +776,7 @@ plt.plot(x,y,marker.show() -

    Meet the Pandas

    +

    Meet the Pandas





    @@ -923,7 +901,7 @@ most operations are vectorized, achieving thereby a high performance when dealin As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in. For multidimensional arrays, we recommend strongly xarray. xarray has much of the same flexibility as pandas, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both pandas and xarray. -

    Reading Data and fitting

    +

    Reading Data and fitting

    In order to study various Machine Learning algorithms, we need to @@ -959,7 +937,7 @@ you can easily implement other algorithms like decision trees and random forests But before we really start with nuclear physics data, let's just look at some simpler polynomial fitting cases, such as, (don't be offended) fitting straight lines! -

    Simple linear regression model using scikit-learn

    +

    Simple linear regression model using scikit-learn

    We start with perhaps our simplest possible example, using Scikit-Learn to perform linear regression analysis on a data set produced by us. @@ -1253,7 +1231,7 @@ plt.show() print (error(y)) -

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    +

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding @@ -1331,7 +1309,7 @@ We could also add a so-called pairing term, which is a correction term that arises from the tendency of proton pairs and neutron pairs to occur. An even number of particles is more stable than an odd number. -

    Organizing our data

    +

    Organizing our data

    Let us start with reading and organizing our data. @@ -1517,7 +1495,7 @@ save_fig("Masses2016") plt.show() -

    Seeing the wood for the trees

    +

    Seeing the wood for the trees

    As a teaser, let us now see how we can do this with decision trees using scikit-learn. Later we will switch to so-called random forests! @@ -1556,7 +1534,7 @@ plt.show() print(np.mean( (Energies-y_1)**2)) -

    And what about using neural networks?

    +

    And what about using neural networks?

    The seaborn package allows us to visualize data in an efficient way. Note that we use scikit-learn's multi-layer perceptron (or feed forward neural network) functionality. @@ -1594,7 +1572,7 @@ ax.set_xlabel(& plt.show() -

    A first summary

    +

    A first summary

    The aim behind these introductory words was to present to you various diff --git a/doc/pub/How2ReadData/html/How2ReadData-reveal.html b/doc/pub/How2ReadData/html/How2ReadData-reveal.html index 3bc2a9175..5353224f2 100644 --- a/doc/pub/How2ReadData/html/How2ReadData-reveal.html +++ b/doc/pub/How2ReadData/html/How2ReadData-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

     
    -

    Aug 13, 2019

    +

    Aug 14, 2019


    Introduction

    @@ -170,7 +170,7 @@ polynomials with random noise added. We will use the Python software package Scikit-Learn and introduce various machine learning algorithms to make fits of the data and predictions. We move thereafter to more interesting -cases such as nuclear binding energies. +cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example). These are examples where we can easily set up the data and then use machine learning algorithms included in for example Scikit-Learn. @@ -180,7 +180,7 @@ These examples will serve us the purpose of getting started. Furthermore, they allow us to catch more than two birds with a stone. They will allow us to bring in some programming specific topics and tools as well as showing the power of various Python -packages for machine learning and statistical data analysis. +libraries for machine learning and statistical data analysis.

    Here, we will mainly focus on two @@ -396,14 +396,14 @@ Here we list several useful Python libraries we strongly recommend (if you use a

    You will also find it convenient to utilize R. We will mainly -use Python during lectures and in various projects and exercises. +use Python during our lectures and in various projects and exercises. Those of you already familiar with R should feel free to continue using R, keeping however an eye on the parallel Python set ups. Similarly, if you are a Python afecionado, feel free to explore R as well. Jupyter/Ipython notebook allows you to run R codes interactively in your -browser. The software library R is tuned to statistically analysis -and allows for an easy usage of the tools we will discuss in these +browser. The software library R is really tailored for statistical data analysis +and allows for an easy usage of the tools and algorithms we will discuss in these lectures.

    @@ -447,7 +447,7 @@ formats, ipython notebooks, latex files, pdf files etc with minimal edits. These

    Numpy examples and Important Matrix and vector handling packages

    -There are several central software packages for linear algebra and eigenvalue problems. Several of the more +There are several central software libraries for linear algebra and eigenvalue problems. Several of the more popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used software package LAPACK, which follows two other popular packages developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here. @@ -482,11 +482,7 @@ $$ \end{bmatrix} $$

     
    - -

    Basic Matrix Features

    -
    -

    The inverse of a matrix is defined by @@ -495,13 +491,7 @@ $$ \mathbf{A}^{-1} \cdot \mathbf{A} = I $$

     
    -

    -

    Basic Matrix Features

    - -

    -

    -Matrix Properties Reminder.

    @@ -518,7 +508,7 @@ $$ -

    Some famous Matrices

    +

    Some famous Matrices

      @@ -541,7 +531,7 @@ $$

    • Banded, block upper triangular, block lower triangular....
    -

    Basic Matrix Features

    +

    More Basic Matrix Features

    @@ -565,7 +555,7 @@ For an \( N\times N \) matrix \( \mathbf{A} \) the following properties are all
    -

    Numpy and arrays

    +

    Numpy and arrays

    Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as

    @@ -650,7 +640,7 @@ x = np.log(np.array([4.0, print(x.itemsize) -

    Matrices in Python

    +

    Matrices in Python

    Having defined vectors, we are now ready to try out matrices. We can @@ -793,7 +783,7 @@ plt.plot(x,y,marker='x') plt.show() -

    Meet the Pandas

    +

    Meet the Pandas





    @@ -918,7 +908,7 @@ most operations are vectorized, achieving thereby a high performance when dealin As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in. For multidimensional arrays, we recommend strongly xarray. xarray has much of the same flexibility as pandas, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both pandas and xarray. -

    Reading Data and fitting

    +

    Reading Data and fitting

    In order to study various Machine Learning algorithms, we need to @@ -954,7 +944,7 @@ you can easily implement other algorithms like decision trees and random forests But before we really start with nuclear physics data, let's just look at some simpler polynomial fitting cases, such as, (don't be offended) fitting straight lines! -

    Simple linear regression model using scikit-learn

    +

    Simple linear regression model using scikit-learn

    We start with perhaps our simplest possible example, using Scikit-Learn to perform linear regression analysis on a data set produced by us. @@ -1266,7 +1256,7 @@ plt.show() print (error(y)) -

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    +

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding @@ -1360,7 +1350,7 @@ We could also add a so-called pairing term, which is a correction term that arises from the tendency of proton pairs and neutron pairs to occur. An even number of particles is more stable than an odd number. -

    Organizing our data

    +

    Organizing our data

    Let us start with reading and organizing our data. @@ -1546,7 +1536,7 @@ save_fig("Masses2016") plt.show() -

    Seeing the wood for the trees

    +

    Seeing the wood for the trees

    As a teaser, let us now see how we can do this with decision trees using scikit-learn. Later we will switch to so-called random forests! @@ -1585,7 +1575,7 @@ plt.show() print(np.mean( (Energies-y_1)**2)) -

    And what about using neural networks?

    +

    And what about using neural networks?

    The seaborn package allows us to visualize data in an efficient way. Note that we use scikit-learn's multi-layer perceptron (or feed forward neural network) functionality. @@ -1623,7 +1613,7 @@ ax.set_xlabel("$\lambda$") plt.show() -

    A first summary

    +

    A first summary

    The aim behind these introductory words was to present to you various diff --git a/doc/pub/How2ReadData/html/How2ReadData-solarized.html b/doc/pub/How2ReadData/html/How2ReadData-solarized.html index 38f4ae343..5b0ab6a7b 100644 --- a/doc/pub/How2ReadData/html/How2ReadData-solarized.html +++ b/doc/pub/How2ReadData/html/How2ReadData-solarized.html @@ -75,27 +75,25 @@ div { text-align: justify; text-justify: inter-word; } None, '___sec8'), ('Basic Matrix Features', 2, None, '___sec9'), - ('Basic Matrix Features', 2, None, '___sec10'), - ('Basic Matrix Features', 2, None, '___sec11'), - ('Some famous Matrices', 2, None, '___sec12'), - ('Basic Matrix Features', 2, None, '___sec13'), - ('Numpy and arrays', 2, None, '___sec14'), - ('Matrices in Python', 2, None, '___sec15'), - ('Meet the Pandas', 2, None, '___sec16'), - ('Reading Data and fitting', 2, None, '___sec17'), + ('Some famous Matrices', 3, None, '___sec10'), + ('More Basic Matrix Features', 3, None, '___sec11'), + ('Numpy and arrays', 2, None, '___sec12'), + ('Matrices in Python', 2, None, '___sec13'), + ('Meet the Pandas', 2, None, '___sec14'), + ('Reading Data and fitting', 2, None, '___sec15'), ('Simple linear regression model using _scikit-learn_', 3, None, - '___sec18'), + '___sec16'), ('To our real data: nuclear binding energies. Brief reminder on ' 'masses and binding energies', 3, None, - '___sec19'), - ('Organizing our data', 3, None, '___sec20'), - ('Seeing the wood for the trees', 3, None, '___sec21'), - ('And what about using neural networks?', 3, None, '___sec22'), - ('A first summary', 2, None, '___sec23')]} + '___sec17'), + ('Organizing our data', 3, None, '___sec18'), + ('Seeing the wood for the trees', 3, None, '___sec19'), + ('And what about using neural networks?', 3, None, '___sec20'), + ('A first summary', 2, None, '___sec21')]} end of tocinfo --> @@ -137,7 +135,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Aug 13, 2019

    +

    Aug 14, 2019


    Introduction

    @@ -159,7 +157,7 @@ polynomials with random noise added. We will use the Python software package Scikit-Learn and introduce various machine learning algorithms to make fits of the data and predictions. We move thereafter to more interesting -cases such as nuclear binding energies. +cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example). These are examples where we can easily set up the data and then use machine learning algorithms included in for example Scikit-Learn. @@ -169,7 +167,7 @@ These examples will serve us the purpose of getting started. Furthermore, they allow us to catch more than two birds with a stone. They will allow us to bring in some programming specific topics and tools as well as showing the power of various Python -packages for machine learning and statistical data analysis. +libraries for machine learning and statistical data analysis.

    Here, we will mainly focus on two @@ -376,14 +374,14 @@ Here we list several useful Python libraries we strongly recommend (if you use a

    You will also find it convenient to utilize R. We will mainly -use Python during lectures and in various projects and exercises. +use Python during our lectures and in various projects and exercises. Those of you already familiar with R should feel free to continue using R, keeping however an eye on the parallel Python set ups. Similarly, if you are a Python afecionado, feel free to explore R as well. Jupyter/Ipython notebook allows you to run R codes interactively in your -browser. The software library R is tuned to statistically analysis -and allows for an easy usage of the tools we will discuss in these +browser. The software library R is really tailored for statistical data analysis +and allows for an easy usage of the tools and algorithms we will discuss in these lectures.

    @@ -427,7 +425,7 @@ formats, ipython notebooks, latex files, pdf files etc with minimal edits. These

    Numpy examples and Important Matrix and vector handling packages

    -There are several central software packages for linear algebra and eigenvalue problems. Several of the more +There are several central software libraries for linear algebra and eigenvalue problems. Several of the more popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used software package LAPACK, which follows two other popular packages developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here. @@ -458,13 +456,6 @@ $$ 0 & 0 & 0 & 1 \end{bmatrix} $$ - - - -

    Basic Matrix Features

    -
    - -

    The inverse of a matrix is defined by @@ -472,15 +463,6 @@ The inverse of a matrix is defined by $$ \mathbf{A}^{-1} \cdot \mathbf{A} = I $$ -

    - - -

    Basic Matrix Features

    - -

    -

    -Matrix Properties Reminder. -

    @@ -499,7 +481,7 @@ $$ -

    Some famous Matrices

    +

    Some famous Matrices

    • Diagonal if \( a_{ij}=0 \) for \( i\ne j \)
      • @@ -513,7 +495,7 @@ $$
    • Banded, block upper triangular, block lower triangular....
    -

    Basic Matrix Features

    +

    More Basic Matrix Features

    @@ -532,7 +514,7 @@ For an \( N\times N \) matrix \( \mathbf{A} \) the following properties are all
    -

    Numpy and arrays

    +

    Numpy and arrays

    Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as

    @@ -617,7 +599,7 @@ x = np.log(np.array([4.0, print(x.itemsize) -

    Matrices in Python

    +

    Matrices in Python

    Having defined vectors, we are now ready to try out matrices. We can @@ -754,7 +736,7 @@ plt.plot(x,y,marker='x') plt.show() -

    Meet the Pandas

    +

    Meet the Pandas





    @@ -879,7 +861,7 @@ most operations are vectorized, achieving thereby a high performance when dealin As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in. For multidimensional arrays, we recommend strongly xarray. xarray has much of the same flexibility as pandas, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both pandas and xarray. -

    Reading Data and fitting

    +

    Reading Data and fitting

    In order to study various Machine Learning algorithms, we need to @@ -915,7 +897,7 @@ you can easily implement other algorithms like decision trees and random forests But before we really start with nuclear physics data, let's just look at some simpler polynomial fitting cases, such as, (don't be offended) fitting straight lines! -

    Simple linear regression model using scikit-learn

    +

    Simple linear regression model using scikit-learn

    We start with perhaps our simplest possible example, using Scikit-Learn to perform linear regression analysis on a data set produced by us. @@ -1209,7 +1191,7 @@ plt.show() print (error(y)) -

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    +

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding @@ -1287,7 +1269,7 @@ We could also add a so-called pairing term, which is a correction term that arises from the tendency of proton pairs and neutron pairs to occur. An even number of particles is more stable than an odd number. -

    Organizing our data

    +

    Organizing our data

    Let us start with reading and organizing our data. @@ -1473,7 +1455,7 @@ save_fig("Masses2016") plt.show() -

    Seeing the wood for the trees

    +

    Seeing the wood for the trees

    As a teaser, let us now see how we can do this with decision trees using scikit-learn. Later we will switch to so-called random forests! @@ -1512,7 +1494,7 @@ plt.show() print(np.mean( (Energies-y_1)**2)) -

    And what about using neural networks?

    +

    And what about using neural networks?

    The seaborn package allows us to visualize data in an efficient way. Note that we use scikit-learn's multi-layer perceptron (or feed forward neural network) functionality. @@ -1550,7 +1532,7 @@ ax.set_xlabel("$\lambda$") plt.show() -

    A first summary

    +

    A first summary

    The aim behind these introductory words was to present to you various diff --git a/doc/pub/How2ReadData/html/How2ReadData.html b/doc/pub/How2ReadData/html/How2ReadData.html index 230e16f8a..24aea122f 100644 --- a/doc/pub/How2ReadData/html/How2ReadData.html +++ b/doc/pub/How2ReadData/html/How2ReadData.html @@ -80,27 +80,25 @@ div { text-align: justify; text-justify: inter-word; } None, '___sec8'), ('Basic Matrix Features', 2, None, '___sec9'), - ('Basic Matrix Features', 2, None, '___sec10'), - ('Basic Matrix Features', 2, None, '___sec11'), - ('Some famous Matrices', 2, None, '___sec12'), - ('Basic Matrix Features', 2, None, '___sec13'), - ('Numpy and arrays', 2, None, '___sec14'), - ('Matrices in Python', 2, None, '___sec15'), - ('Meet the Pandas', 2, None, '___sec16'), - ('Reading Data and fitting', 2, None, '___sec17'), + ('Some famous Matrices', 3, None, '___sec10'), + ('More Basic Matrix Features', 3, None, '___sec11'), + ('Numpy and arrays', 2, None, '___sec12'), + ('Matrices in Python', 2, None, '___sec13'), + ('Meet the Pandas', 2, None, '___sec14'), + ('Reading Data and fitting', 2, None, '___sec15'), ('Simple linear regression model using _scikit-learn_', 3, None, - '___sec18'), + '___sec16'), ('To our real data: nuclear binding energies. Brief reminder on ' 'masses and binding energies', 3, None, - '___sec19'), - ('Organizing our data', 3, None, '___sec20'), - ('Seeing the wood for the trees', 3, None, '___sec21'), - ('And what about using neural networks?', 3, None, '___sec22'), - ('A first summary', 2, None, '___sec23')]} + '___sec17'), + ('Organizing our data', 3, None, '___sec18'), + ('Seeing the wood for the trees', 3, None, '___sec19'), + ('And what about using neural networks?', 3, None, '___sec20'), + ('A first summary', 2, None, '___sec21')]} end of tocinfo --> @@ -142,7 +140,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Aug 13, 2019

    +

    Aug 14, 2019


    Introduction

    @@ -164,7 +162,7 @@ polynomials with random noise added. We will use the Python software package Scikit-Learn and introduce various machine learning algorithms to make fits of the data and predictions. We move thereafter to more interesting -cases such as nuclear binding energies. +cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example). These are examples where we can easily set up the data and then use machine learning algorithms included in for example Scikit-Learn. @@ -174,7 +172,7 @@ These examples will serve us the purpose of getting started. Furthermore, they allow us to catch more than two birds with a stone. They will allow us to bring in some programming specific topics and tools as well as showing the power of various Python -packages for machine learning and statistical data analysis. +libraries for machine learning and statistical data analysis.

    Here, we will mainly focus on two @@ -381,14 +379,14 @@ Here we list several useful Python libraries we strongly recommend (if you use a

    You will also find it convenient to utilize R. We will mainly -use Python during lectures and in various projects and exercises. +use Python during our lectures and in various projects and exercises. Those of you already familiar with R should feel free to continue using R, keeping however an eye on the parallel Python set ups. Similarly, if you are a Python afecionado, feel free to explore R as well. Jupyter/Ipython notebook allows you to run R codes interactively in your -browser. The software library R is tuned to statistically analysis -and allows for an easy usage of the tools we will discuss in these +browser. The software library R is really tailored for statistical data analysis +and allows for an easy usage of the tools and algorithms we will discuss in these lectures.

    @@ -432,7 +430,7 @@ formats, ipython notebooks, latex files, pdf files etc with minimal edits. These

    Numpy examples and Important Matrix and vector handling packages

    -There are several central software packages for linear algebra and eigenvalue problems. Several of the more +There are several central software libraries for linear algebra and eigenvalue problems. Several of the more popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used software package LAPACK, which follows two other popular packages developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here. @@ -463,13 +461,6 @@ $$ 0 & 0 & 0 & 1 \end{bmatrix} $$ - - - -

    Basic Matrix Features

    -
    - -

    The inverse of a matrix is defined by @@ -477,15 +468,6 @@ The inverse of a matrix is defined by $$ \mathbf{A}^{-1} \cdot \mathbf{A} = I $$ -

    - - -

    Basic Matrix Features

    - -

    -

    -Matrix Properties Reminder. -

    @@ -504,7 +486,7 @@ $$ -

    Some famous Matrices

    +

    Some famous Matrices

    • Diagonal if \( a_{ij}=0 \) for \( i\ne j \)
      • @@ -518,7 +500,7 @@ $$
    • Banded, block upper triangular, block lower triangular....
    -

    Basic Matrix Features

    +

    More Basic Matrix Features

    @@ -537,7 +519,7 @@ For an \( N\times N \) matrix \( \mathbf{A} \) the following properties are all
    -

    Numpy and arrays

    +

    Numpy and arrays

    Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as

    @@ -622,7 +604,7 @@ x = np.l print(x.itemsize) -

    Matrices in Python

    +

    Matrices in Python

    Having defined vectors, we are now ready to try out matrices. We can @@ -759,7 +741,7 @@ plt.plot(x,y,marker.show() -

    Meet the Pandas

    +

    Meet the Pandas





    @@ -884,7 +866,7 @@ most operations are vectorized, achieving thereby a high performance when dealin As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in. For multidimensional arrays, we recommend strongly xarray. xarray has much of the same flexibility as pandas, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both pandas and xarray. -

    Reading Data and fitting

    +

    Reading Data and fitting

    In order to study various Machine Learning algorithms, we need to @@ -920,7 +902,7 @@ you can easily implement other algorithms like decision trees and random forests But before we really start with nuclear physics data, let's just look at some simpler polynomial fitting cases, such as, (don't be offended) fitting straight lines! -

    Simple linear regression model using scikit-learn

    +

    Simple linear regression model using scikit-learn

    We start with perhaps our simplest possible example, using Scikit-Learn to perform linear regression analysis on a data set produced by us. @@ -1214,7 +1196,7 @@ plt.show() print (error(y)) -

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    +

    To our real data: nuclear binding energies. Brief reminder on masses and binding energies

    Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding @@ -1292,7 +1274,7 @@ We could also add a so-called pairing term, which is a correction term that arises from the tendency of proton pairs and neutron pairs to occur. An even number of particles is more stable than an odd number. -

    Organizing our data

    +

    Organizing our data

    Let us start with reading and organizing our data. @@ -1478,7 +1460,7 @@ save_fig("Masses2016") plt.show() -

    Seeing the wood for the trees

    +

    Seeing the wood for the trees

    As a teaser, let us now see how we can do this with decision trees using scikit-learn. Later we will switch to so-called random forests! @@ -1517,7 +1499,7 @@ plt.show() print(np.mean( (Energies-y_1)**2)) -

    And what about using neural networks?

    +

    And what about using neural networks?

    The seaborn package allows us to visualize data in an efficient way. Note that we use scikit-learn's multi-layer perceptron (or feed forward neural network) functionality. @@ -1555,7 +1537,7 @@ ax.set_xlabel(& plt.show() -

    A first summary

    +

    A first summary

    The aim behind these introductory words was to present to you various diff --git a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb index a17d0d7a4..6ee1372cb 100644 --- a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb +++ b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb @@ -10,7 +10,7 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Aug 13, 2019**\n", + "Date: **Aug 14, 2019**\n", "\n", "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -36,7 +36,7 @@ "software package [Scikit-Learn](http://scikit-learn.org/stable/) and\n", "introduce various machine learning algorithms to make fits of\n", "the data and predictions. We move thereafter to more interesting\n", - "cases such as nuclear binding energies.\n", + "cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example).\n", "These are examples where we can easily set up the data and\n", "then use machine learning algorithms included in for example\n", "**Scikit-Learn**. \n", @@ -45,7 +45,7 @@ "started. Furthermore, they allow us to catch more than two birds with\n", "a stone. They will allow us to bring in some programming specific\n", "topics and tools as well as showing the power of various Python \n", - "packages for machine learning and statistical data analysis. \n", + "libraries for machine learning and statistical data analysis. \n", "\n", "Here, we will mainly focus on two\n", "specific Python packages for Machine Learning, Scikit-Learn and\n", @@ -249,14 +249,14 @@ "## Installing R, C++, cython or Julia\n", "\n", "You will also find it convenient to utilize **R**. We will mainly\n", - "use Python during lectures and in various projects and exercises.\n", + "use Python during our lectures and in various projects and exercises.\n", "Those of you\n", "already familiar with **R** should feel free to continue using **R**, keeping\n", "however an eye on the parallel Python set ups. Similarly, if you are a\n", "Python afecionado, feel free to explore **R** as well. Jupyter/Ipython\n", "notebook allows you to run **R** codes interactively in your\n", - "browser. The software library **R** is tuned to statistically analysis\n", - "and allows for an easy usage of the tools we will discuss in these\n", + "browser. The software library **R** is really tailored for statistical data analysis\n", + "and allows for an easy usage of the tools and algorithms we will discuss in these\n", "lectures.\n", "\n", "To install **R** with Jupyter notebook \n", @@ -307,7 +307,7 @@ "\n", "## Numpy examples and Important Matrix and vector handling packages\n", "\n", - "There are several central software packages for linear algebra and eigenvalue problems. Several of the more\n", + "There are several central software libraries for linear algebra and eigenvalue problems. Several of the more\n", "popular ones have been wrapped into ofter software packages like those from the widely used text **Numerical Recipes**. The original source codes in many of the available packages are often taken from the widely used\n", "software package LAPACK, which follows two other popular packages\n", "developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here.\n", @@ -347,8 +347,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Basic Matrix Features\n", - "\n", "The inverse of a matrix is defined by" ] }, @@ -365,11 +363,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Basic Matrix Features\n", - "\n", - "**Matrix Properties Reminder.**\n", - "\n", - "\n", "

    \n", "\n", "\n", @@ -386,7 +379,7 @@ "\n", "\n", "\n", - "## Some famous Matrices\n", + "### Some famous Matrices\n", "\n", " * Diagonal if $a_{ij}=0$ for $i\\ne j$\n", "\n", @@ -406,7 +399,7 @@ "\n", " * Banded, block upper triangular, block lower triangular....\n", "\n", - "## Basic Matrix Features\n", + "### More Basic Matrix Features\n", "\n", "**Some Equivalent Statements.**\n", "\n", diff --git a/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz b/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz index 4e4f7e1ff..1823d4414 100644 Binary files a/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz and b/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz differ diff --git a/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf b/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf index 0b0bd4ff9..bc2461995 100644 Binary files a/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf and b/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf differ diff --git a/doc/src/How2ReadData/How2ReadData.do.txt b/doc/src/How2ReadData/How2ReadData.do.txt index 12541a443..53b452322 100644 --- a/doc/src/How2ReadData/How2ReadData.do.txt +++ b/doc/src/How2ReadData/How2ReadData.do.txt @@ -22,7 +22,7 @@ polynomials with random noise added. We will use the Python software package "Scikit-Learn":"http://scikit-learn.org/stable/" and introduce various machine learning algorithms to make fits of the data and predictions. We move thereafter to more interesting -cases such as nuclear binding energies. +cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example). These are examples where we can easily set up the data and then use machine learning algorithms included in for example _Scikit-Learn_. @@ -31,7 +31,7 @@ These examples will serve us the purpose of getting started. Furthermore, they allow us to catch more than two birds with a stone. They will allow us to bring in some programming specific topics and tools as well as showing the power of various Python -packages for machine learning and statistical data analysis. +libraries for machine learning and statistical data analysis. Here, we will mainly focus on two specific Python packages for Machine Learning, Scikit-Learn and @@ -226,14 +226,14 @@ Here we list several useful Python libraries we strongly recommend (if you use a ===== Installing R, C++, cython or Julia ===== You will also find it convenient to utilize _R_. We will mainly -use Python during lectures and in various projects and exercises. +use Python during our lectures and in various projects and exercises. Those of you already familiar with _R_ should feel free to continue using _R_, keeping however an eye on the parallel Python set ups. Similarly, if you are a Python afecionado, feel free to explore _R_ as well. Jupyter/Ipython notebook allows you to run _R_ codes interactively in your -browser. The software library _R_ is tuned to statistically analysis -and allows for an easy usage of the tools we will discuss in these +browser. The software library _R_ is really tailored for statistical data analysis +and allows for an easy usage of the tools and algorithms we will discuss in these lectures. To install _R_ with Jupyter notebook @@ -276,7 +276,7 @@ formats, ipython notebooks, latex files, pdf files etc with minimal edits. These ===== Numpy examples and Important Matrix and vector handling packages ===== -There are several central software packages for linear algebra and eigenvalue problems. Several of the more +There are several central software libraries for linear algebra and eigenvalue problems. Several of the more popular ones have been wrapped into ofter software packages like those from the widely used text _Numerical Recipes_. The original source codes in many of the available packages are often taken from the widely used software package LAPACK, which follows two other popular packages developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here. @@ -305,10 +305,8 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he \end{bmatrix} \] !et -!eblock -===== Basic Matrix Features ===== -!bblock + The inverse of a matrix is defined by @@ -317,13 +315,8 @@ The inverse of a matrix is defined by \mathbf{A}^{-1} \cdot \mathbf{A} = I \] !et -!eblock -===== Basic Matrix Features ===== - -!bblock Matrix Properties Reminder - |----------------------------------------------------------------------| | Relations | Name | matrix elements | |----------------------------------------------------------------------| @@ -337,7 +330,7 @@ The inverse of a matrix is defined by !eblock -===== Some famous Matrices ===== +=== Some famous Matrices === * Diagonal if $a_{ij}=0$ for $i\ne j$ * Upper triangular if $a_{ij}=0$ for $i > j$ @@ -350,7 +343,7 @@ The inverse of a matrix is defined by * Banded, block upper triangular, block lower triangular.... -===== Basic Matrix Features ===== +=== More Basic Matrix Features === !bblock Some Equivalent Statements For an $N\times N$ matrix $\mathbf{A}$ the following properties are all equivalent
    Relations Name matrix elements