lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

typos

Browse Source
This commit is contained in:
mhjensen
2020-01-02 10:58:08 +01:00
parent a9ea09f8fd
commit 5456959e12
8 changed files with 18 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/pub/DimRed/html/._DimRed-bs018.html
+3 -2
View File
@@ -228,6 +228,7 @@ Note that the function <b>multivariate</b> returns also the covariance discussed
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">pandas</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">pd</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">IPython.display</span> <span style="color: #008000; font-weight: bold">import</span> display
n <span style="color: #666666">=</span> <span style="color: #666666">10000</span>
mean <span style="color: #666666">=</span> (<span style="color: #666666">-1</span>, <span style="color: #666666">2</span>)
@@ -298,8 +299,8 @@ Our own code here is not very elegant and asks for improvements.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,[0]]</span>
y <span style="color: #666666">=</span> X_centered[:,[<span style="color: #666666">1</span>]]
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,0]</span>
y <span style="color: #666666">=</span> X_centered[:,<span style="color: #666666">1</span>]
Cov <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #666666">2</span>,<span style="color: #666666">2</span>))
Cov[<span style="color: #666666">0</span>,<span style="color: #666666">1</span>] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>sum(x<span style="color: #666666">.</span>T<span style="color: #AA22FF">@y</span>)<span style="color: #666666">/</span>(n<span style="color: #666666">-1.0</span>)
Cov[<span style="color: #666666">0</span>,<span style="color: #666666">0</span>] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>sum(x<span style="color: #666666">.</span>T<span style="color: #AA22FF">@x</span>)<span style="color: #666666">/</span>(n<span style="color: #666666">-1.0</span>)
doc/pub/DimRed/html/DimRed-reveal.html
+3 -2
View File
@@ -1034,6 +1034,7 @@ Note that the function <b>multivariate</b> returns also the covariance discussed
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">pandas</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">pd</span>
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">IPython.display</span> <span style="color: #8B008B; font-weight: bold">import</span> display
n = <span style="color: #B452CD">10000</span>
mean = (-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">2</span>)
@@ -1110,8 +1111,8 @@ Our own code here is not very elegant and asks for improvements.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #228B22"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,[0]]</span>
y = X_centered[:,[<span style="color: #B452CD">1</span>]]
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #228B22"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,0]</span>
y = X_centered[:,<span style="color: #B452CD">1</span>]
Cov = np.zeros((<span style="color: #B452CD">2</span>,<span style="color: #B452CD">2</span>))
Cov[<span style="color: #B452CD">0</span>,<span style="color: #B452CD">1</span>] = np.sum(x.T<span style="color: #707a7c">@y</span>)/(n-<span style="color: #B452CD">1.0</span>)
Cov[<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0</span>] = np.sum(x.T<span style="color: #707a7c">@x</span>)/(n-<span style="color: #B452CD">1.0</span>)
doc/pub/DimRed/html/DimRed-solarized.html
+3 -2
View File
@@ -1014,6 +1014,7 @@ Note that the function <b>multivariate</b> returns also the covariance discussed
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">pandas</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">pd</span>
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">IPython.display</span> <span style="color: #8B008B; font-weight: bold">import</span> display
n = <span style="color: #B452CD">10000</span>
mean = (-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">2</span>)
@@ -1084,8 +1085,8 @@ Our own code here is not very elegant and asks for improvements.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #228B22"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,[0]]</span>
y = X_centered[:,[<span style="color: #B452CD">1</span>]]
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #228B22"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,0]</span>
y = X_centered[:,<span style="color: #B452CD">1</span>]
Cov = np.zeros((<span style="color: #B452CD">2</span>,<span style="color: #B452CD">2</span>))
Cov[<span style="color: #B452CD">0</span>,<span style="color: #B452CD">1</span>] = np.sum(x.T<span style="color: #707a7c">@y</span>)/(n-<span style="color: #B452CD">1.0</span>)
Cov[<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0</span>] = np.sum(x.T<span style="color: #707a7c">@x</span>)/(n-<span style="color: #B452CD">1.0</span>)
doc/pub/DimRed/html/DimRed.html
+3 -2
View File
@@ -1019,6 +1019,7 @@ Note that the function <b>multivariate</b> returns also the covariance discussed
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">pandas</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">pd</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">IPython.display</span> <span style="color: #008000; font-weight: bold">import</span> display
n <span style="color: #666666">=</span> <span style="color: #666666">10000</span>
mean <span style="color: #666666">=</span> (<span style="color: #666666">-1</span>, <span style="color: #666666">2</span>)
@@ -1089,8 +1090,8 @@ Our own code here is not very elegant and asks for improvements.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,[0]]</span>
y <span style="color: #666666">=</span> X_centered[:,[<span style="color: #666666">1</span>]]
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,0]</span>
y <span style="color: #666666">=</span> X_centered[:,<span style="color: #666666">1</span>]
Cov <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #666666">2</span>,<span style="color: #666666">2</span>))
Cov[<span style="color: #666666">0</span>,<span style="color: #666666">1</span>] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>sum(x<span style="color: #666666">.</span>T<span style="color: #AA22FF">@y</span>)<span style="color: #666666">/</span>(n<span style="color: #666666">-1.0</span>)
Cov[<span style="color: #666666">0</span>,<span style="color: #666666">0</span>] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>sum(x<span style="color: #666666">.</span>T<span style="color: #AA22FF">@x</span>)<span style="color: #666666">/</span>(n<span style="color: #666666">-1.0</span>)
doc/pub/DimRed/ipynb/DimRed.ipynb
+3 -2
View File
@@ -1107,6 +1107,7 @@
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib as plt\n",
"from IPython.display import display\n",
"n = 10000\n",
"mean = (-1, 2)\n",
@@ -1239,8 +1240,8 @@
},
"outputs": [],
"source": [
"# extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,[0]]\n",
"y = X_centered[:,[1]]\n",
"# extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,0]\n",
"y = X_centered[:,1]\n",
"Cov = np.zeros((2,2))\n",
"Cov[0,1] = np.sum(x.T@y)/(n-1.0)\n",
"Cov[0,0] = np.sum(x.T@x)/(n-1.0)\n",
doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz
BIN
View File
Binary file not shown.
doc/pub/DimRed/pdf/DimRed-minted.pdf
BIN
View File
Binary file not shown.
doc/src/DimRed/DimRed.do.txt
+3 -2
View File
@@ -781,6 +781,7 @@ Note that the function _multivariate_ returns also the covariance discussed abov
!bc pycod
import numpy as np
import pandas as pd
import matplotlib as plt
from IPython.display import display
n = 10000
mean = (-1, 2)
@@ -843,8 +844,8 @@ print(np.cov(X_centered.T))
Note that the way we define the covariance matrix here has a factor $n-1$ instead of $n$.
Our own code here is not very elegant and asks for improvements.
!bc pycod
# extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,[0]]
y = X_centered[:,[1]]
# extract the relevant columns from the centered design matrix of dim n x 2 x = X_centered[:,0]
y = X_centered[:,1]
Cov = np.zeros((2,2))
Cov[0,1] = np.sum(x.T@y)/(n-1.0)
Cov[0,0] = np.sum(x.T@x)/(n-1.0)