From d998ff24f08e4f3a20b299f16d8ee38405075121 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Sat, 4 Sep 2021 21:30:40 +0200 Subject: [PATCH] correcting cov matrix in week 34 --- doc/pub/week34/html/._week34-bs000.html | 2 +- doc/pub/week34/html/._week34-bs035.html | 9 +++------ doc/pub/week34/html/week34-bs.html | 2 +- doc/pub/week34/html/week34-reveal.html | 11 ++++------- doc/pub/week34/html/week34-solarized.html | 11 ++++------- doc/pub/week34/html/week34.html | 11 ++++------- doc/pub/week34/ipynb/ipynb-week34-src.tar.gz | Bin 103349 -> 103349 bytes doc/pub/week34/ipynb/week34.ipynb | 11 ++++------- doc/src/week34/week34.do.txt | 9 +++------ 9 files changed, 24 insertions(+), 42 deletions(-) diff --git a/doc/pub/week34/html/._week34-bs000.html b/doc/pub/week34/html/._week34-bs000.html index 1c2ca5bc1..05510b462 100644 --- a/doc/pub/week34/html/._week34-bs000.html +++ b/doc/pub/week34/html/._week34-bs000.html @@ -384,7 +384,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Sep 2, 2021

+

Sep 4, 2021


diff --git a/doc/pub/week34/html/._week34-bs035.html b/doc/pub/week34/html/._week34-bs035.html index 68965d064..190e60cef 100644 --- a/doc/pub/week34/html/._week34-bs035.html +++ b/doc/pub/week34/html/._week34-bs035.html @@ -451,12 +451,9 @@ $$ The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values. The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \) $$ -\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\ - x_1 & y_1 & z_1 \\ - x_2 & y_2 & z_2 \\ - \dots & \dots & \dots \\ - x_{n-2} & y_{n-2} & z_{n-2} \\ - x_{n-1} & y_{n-1} & z_{n-1} +\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\ + y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\ + z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\ \end{bmatrix}, $$ diff --git a/doc/pub/week34/html/week34-bs.html b/doc/pub/week34/html/week34-bs.html index 1c2ca5bc1..05510b462 100644 --- a/doc/pub/week34/html/week34-bs.html +++ b/doc/pub/week34/html/week34-bs.html @@ -384,7 +384,7 @@ MathJax.Hub.Config({

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

-

Sep 2, 2021

+

Sep 4, 2021


diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html index aed011496..7e2e57557 100644 --- a/doc/pub/week34/html/week34-reveal.html +++ b/doc/pub/week34/html/week34-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({

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

 
-

Sep 2, 2021

+

Sep 4, 2021


@@ -1257,12 +1257,9 @@ The Numpy function np.cov calculates the covariance elements using the fa The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \)

 
$$ -\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\ - x_1 & y_1 & z_1 \\ - x_2 & y_2 & z_2 \\ - \dots & \dots & \dots \\ - x_{n-2} & y_{n-2} & z_{n-2} \\ - x_{n-1} & y_{n-1} & z_{n-1} +\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\ + y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\ + z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\ \end{bmatrix}, $$

 
diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html index ac99ec414..97ee03361 100644 --- a/doc/pub/week34/html/week34-solarized.html +++ b/doc/pub/week34/html/week34-solarized.html @@ -300,7 +300,7 @@ MathJax.Hub.Config({

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

-

Sep 2, 2021

+

Sep 4, 2021












@@ -1362,12 +1362,9 @@ $$ The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values. The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \) $$ -\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\ - x_1 & y_1 & z_1 \\ - x_2 & y_2 & z_2 \\ - \dots & \dots & \dots \\ - x_{n-2} & y_{n-2} & z_{n-2} \\ - x_{n-1} & y_{n-1} & z_{n-1} +\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\ + y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\ + z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\ \end{bmatrix}, $$ diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html index fe4613252..5e2dee325 100644 --- a/doc/pub/week34/html/week34.html +++ b/doc/pub/week34/html/week34.html @@ -305,7 +305,7 @@ MathJax.Hub.Config({

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

-

Sep 2, 2021

+

Sep 4, 2021












@@ -1367,12 +1367,9 @@ $$ The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values. The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \) $$ -\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\ - x_1 & y_1 & z_1 \\ - x_2 & y_2 & z_2 \\ - \dots & \dots & \dots \\ - x_{n-2} & y_{n-2} & z_{n-2} \\ - x_{n-1} & y_{n-1} & z_{n-1} +\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\ + y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\ + z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\ \end{bmatrix}, $$ diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz index ff964c767a5e1eb16386d00388e57217cba9aa08..8d89d0ef9875ca845109d77a4ef70beb5894ceeb 100644 GIT binary patch delta 21 dcmdnGoNeoJHg@@L4hH{|#*OS-*%{YO1^`no2TuS1 delta 21 dcmdnGoNeoJHg@@L4u \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: **Sep 2, 2021**\n", + "Date: **Sep 4, 2021**\n", "\n", "Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -1175,12 +1175,9 @@ "metadata": {}, "source": [ "$$\n", - "\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 & z_0 \\\\\n", - " x_1 & y_1 & z_1 \\\\\n", - " x_2 & y_2 & z_2 \\\\\n", - " \\dots & \\dots & \\dots \\\\\n", - " x_{n-2} & y_{n-2} & z_{n-2} \\\\\n", - " x_{n-1} & y_{n-1} & z_{n-1}\n", + "\\boldsymbol{W} = \\begin{bmatrix} x_0 & x_1 & x_2 & \\dots & x_{n-2} & x_{n-1} \\\\\n", + " y_0 & y_1 & y_2 & \\dots & y_{n-2} & y_{n-1} \\\\\n", + "\t\t\t z_0 & z_1 & z_2 & \\dots & z_{n-2} & z_{n-1} \\\\\n", " \\end{bmatrix},\n", "$$" ] diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt index af2347939..868cb0788 100644 --- a/doc/src/week34/week34.do.txt +++ b/doc/src/week34/week34.do.txt @@ -854,12 +854,9 @@ The Numpy function _np.cov_ calculates the covariance elements using the factor The following simple function uses the _np.vstack_ function which takes each vector of dimension $1\times n$ and produces a $3\times n$ matrix $\bm{W}$ !bt \[ -\bm{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\ - x_1 & y_1 & z_1 \\ - x_2 & y_2 & z_2 \\ - \dots & \dots & \dots \\ - x_{n-2} & y_{n-2} & z_{n-2} \\ - x_{n-1} & y_{n-1} & z_{n-1} +\bm{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\ + y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\ + z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\ \end{bmatrix}, \] !et