From 011f78752836bc25ae84696e7615e7d9d112fbc6 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Thu, 2 Jan 2020 11:07:07 +0100 Subject: [PATCH] typos --- doc/pub/DimRed/html/._DimRed-bs018.html | 5 +++-- doc/pub/DimRed/html/DimRed-reveal.html | 5 +++-- doc/pub/DimRed/html/DimRed-solarized.html | 5 +++-- doc/pub/DimRed/html/DimRed.html | 5 +++-- doc/pub/DimRed/ipynb/DimRed.ipynb | 5 +++-- doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz | Bin 191 -> 191 bytes doc/pub/DimRed/pdf/DimRed-minted.pdf | Bin 263781 -> 263781 bytes doc/src/DimRed/DimRed.do.txt | 5 +++-- 8 files changed, 18 insertions(+), 12 deletions(-) diff --git a/doc/pub/DimRed/html/._DimRed-bs018.html b/doc/pub/DimRed/html/._DimRed-bs018.html index 99819576f..a11bbb777 100644 --- a/doc/pub/DimRed/html/._DimRed-bs018.html +++ b/doc/pub/DimRed/html/._DimRed-bs018.html @@ -228,7 +228,7 @@ Note that the function multivariate returns also the covariance discussed 
import numpy as np
 import pandas as pd
-import matplotlib as plt
+import matplotlib.pyplot as plt
 from IPython.display import display
 n = 10000
 mean = (-1, 2)
@@ -299,7 +299,8 @@ Our own code here is not very elegant and asks for improvements.
 

 
 
-
# extract the relevant columns from the centered design matrix of dim n x 2                                                      x = X_centered[:,0]
+
# 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)
diff --git a/doc/pub/DimRed/html/DimRed-reveal.html b/doc/pub/DimRed/html/DimRed-reveal.html
index 081966b55..5077e5fff 100644
--- a/doc/pub/DimRed/html/DimRed-reveal.html
+++ b/doc/pub/DimRed/html/DimRed-reveal.html
@@ -1034,7 +1034,7 @@ Note that the function multivariate returns also the covariance discussed
 
 
import numpy as np
 import pandas as pd
-import matplotlib as plt
+import matplotlib.pyplot as plt
 from IPython.display import display
 n = 10000
 mean = (-1, 2)
@@ -1111,7 +1111,8 @@ Our own code here is not very elegant and asks for improvements.
 

 
 
-
# extract the relevant columns from the centered design matrix of dim n x 2                                                      x = X_centered[:,0]
+
# 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)
diff --git a/doc/pub/DimRed/html/DimRed-solarized.html b/doc/pub/DimRed/html/DimRed-solarized.html
index 20dd9cedb..7f81d4685 100644
--- a/doc/pub/DimRed/html/DimRed-solarized.html
+++ b/doc/pub/DimRed/html/DimRed-solarized.html
@@ -1014,7 +1014,7 @@ Note that the function multivariate returns also the covariance discussed
 
 
import numpy as np
 import pandas as pd
-import matplotlib as plt
+import matplotlib.pyplot as plt
 from IPython.display import display
 n = 10000
 mean = (-1, 2)
@@ -1085,7 +1085,8 @@ Our own code here is not very elegant and asks for improvements.
 

 
 
-
# extract the relevant columns from the centered design matrix of dim n x 2                                                      x = X_centered[:,0]
+
# 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)
diff --git a/doc/pub/DimRed/html/DimRed.html b/doc/pub/DimRed/html/DimRed.html
index 25557ca0c..324cbc7d7 100644
--- a/doc/pub/DimRed/html/DimRed.html
+++ b/doc/pub/DimRed/html/DimRed.html
@@ -1019,7 +1019,7 @@ Note that the function multivariate returns also the covariance discussed
 
 
import numpy as np
 import pandas as pd
-import matplotlib as plt
+import matplotlib.pyplot as plt
 from IPython.display import display
 n = 10000
 mean = (-1, 2)
@@ -1090,7 +1090,8 @@ Our own code here is not very elegant and asks for improvements.
 

 
 
-
# extract the relevant columns from the centered design matrix of dim n x 2                                                      x = X_centered[:,0]
+
# 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)
diff --git a/doc/pub/DimRed/ipynb/DimRed.ipynb b/doc/pub/DimRed/ipynb/DimRed.ipynb
index 8dd93a16a..f51883b28 100644
--- a/doc/pub/DimRed/ipynb/DimRed.ipynb
+++ b/doc/pub/DimRed/ipynb/DimRed.ipynb
@@ -1107,7 +1107,7 @@
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import matplotlib as plt\n",
+    "import matplotlib.pyplot as plt\n",
     "from IPython.display import display\n",
     "n = 10000\n",
     "mean = (-1, 2)\n",
@@ -1240,7 +1240,8 @@
    },
    "outputs": [],
    "source": [
-    "# extract the relevant columns from the centered design matrix of dim n x 2                                                      x = X_centered[:,0]\n",
+    "# extract the relevant columns from the centered design matrix of dim n x 2\n",
+    "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",
diff --git a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz
index 3f28ba9d71577b3c1c9674998de00ed53fc9ccce..e774e0d7c7a6ab6a81665c215d8c0a00e306e400 100644
GIT binary patch
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diff --git a/doc/pub/DimRed/pdf/DimRed-minted.pdf b/doc/pub/DimRed/pdf/DimRed-minted.pdf
index 5b575e4c297647fdb2d6fb155a7d3408d9f27374..1112487ce2c60d342ee9e6c42a0dd4a3c8c5066e 100644
GIT binary patch
delta 136
zcmaFbBk;6GprM7ag=q`3?l%oX12a<#Z39Df0|RwUE`8tp6qm%3R0RzeDyKOq@)N%*{i|%uEgK6l@47*)GGv
H;>iR6@E{@+

delta 136
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diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt
index e5d74688d..e6cc7ee23 100644
--- a/doc/src/DimRed/DimRed.do.txt
+++ b/doc/src/DimRed/DimRed.do.txt
@@ -781,7 +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
+import matplotlib.pyplot as plt
 from IPython.display import display
 n = 10000
 mean = (-1, 2)
@@ -844,7 +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]
+# 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)