ch02 code ready

chaneetsk · Jun 10, 2019 · 996f958 · 996f958
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commit 996f958
Showing 59 changed files with 5,760 additions and 1 deletion.
diff --git a/.convert_notebook_to_script.py b/.convert_notebook_to_script.py
@@ -0,0 +1,78 @@
+# Simple helper script to convert
+# a Jupyter notebook to Python
+#
+# Sebastian Raschka, 2017
+
+
+import argparse
+import os
+import subprocess
+
+
+def convert(input_path, output_path):
+    subprocess.call(['jupyter', 'nbconvert', '--to', 'script',
+                     input_path, '--output', output_path])
+
+
+def cleanup(path):
+
+    skip_lines_startwith = ('Image(filename=',
+                            '# In[',
+                            '# <hr>',
+                            'from IPython.display import Image',
+                            'get_ipython()',
+                            '# <br>')
+
+    clean_content = []
+    imports = []
+    existing_imports = set()
+    with open(path, 'r') as f:
+        next(f)
+        next(f)
+        for line in f:
+            line = line.rstrip(' ')
+            if line.startswith(skip_lines_startwith):
+                continue
+            if line.startswith('import ') or (
+                    'from ' in line and 'import ' in line):
+                if 'from __future__ import print_function' in line:
+                    if line != imports[0]:
+                        imports.insert(0, line)
+                else:
+                    if line.strip() not in existing_imports:
+                        imports.append(line)
+                        existing_imports.add(line.strip())
+            else:
+                clean_content.append(line)
+
+    clean_content = ['# coding: utf-8\n\n\n'] + imports + clean_content
+
+    with open(path, 'w') as f:
+        for line in clean_content:
+            f.write(line)
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(
+            description='Convert Jupyter notebook to Python script.',
+            formatter_class=argparse.RawTextHelpFormatter)
+
+    parser.add_argument('-i', '--input',
+                        required=True,
+                        help='Path to the Jupyter Notebook file')
+
+    parser.add_argument('-o', '--output',
+                        required=True,
+                        help='Path to the Python script file')
+
+    parser.add_argument('-v', '--version',
+                        action='version',
+                        version='v. 0.1')
+
+    args = parser.parse_args()
+
+    convert(input_path=args.input,
+            output_path=os.path.splitext(args.output)[0])
+
+    cleanup(args.output)
diff --git a/ch01/ch01.ipynb b/ch01/ch01.ipynb
@@ -564,7 +564,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

diff --git a/ch02/README.md b/ch02/README.md
@@ -0,0 +1,48 @@
+Python Machine Learning - Code Examples
+
+
+##  Chapter 2: Training Simple Machine Learning Algorithms for Classification
+
+### Chapter Outline
+
+- Artificial neurons – a brief glimpse into the early history of machine learning
+  - The formal definition of an artificial neuron
+  - The perceptron learning rule
+- Implementing a perceptron learning algorithm in Python 
+  - An object-oriented perceptron API
+  - Training a perceptron model on the Iris dataset
+- Adaptive linear neurons and the convergence of learning
+  - Minimizing cost functions with gradient descent
+  - Implementing an Adaptive Linear Neuron in Python
+  - Improving gradient descent through feature scaling
+  - Large scale machine learning and stochastic gradient descent
+- Summary
+
+### A note on using the code examples
+
+The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
+
+![](images/jupyter-example-1.png)
+
+
+
+Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
+
+    conda install jupyter notebook
+
+Then you can launch jupyter notebook by executing
+
+    jupyter notebook
+
+A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
+
+**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
+
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch02.ipynb`](ch02.ipynb))**
+
+In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
+
+![](images/jupyter-example-2.png)
+
+
+When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor. 
diff --git a/ch02/ch02.ipynb b/ch02/ch02.ipynb
diff --git a/ch02/ch02.py b/ch02/ch02.py
diff --git a/ch02/images/02-to-address.txt b/ch02/images/02-to-address.txt
@@ -0,0 +1,25 @@
+<--------------------------------------------------------------------------------------------
+Alfredo Canziani
+
+Ch2: you never explained ravel and zip
+
+----------------------
+
+activation function should be used in adaline
+
+=================
+
+mention logistic regression implementation
+
+
+=========
+
+vector scaling, see mail by makarov@cl.uzh.ch
+
+=========
+
+make datasets locally available
+
+========
+
+badly vs well-conditioned problem
diff --git a/ch02/images/02_01.png b/ch02/images/02_01.png
diff --git a/ch02/images/02_02.graffle b/ch02/images/02_02.graffle
diff --git a/ch02/images/02_02.png b/ch02/images/02_02.png
diff --git a/ch02/images/02_03.graffle b/ch02/images/02_03.graffle
diff --git a/ch02/images/02_03.png b/ch02/images/02_03.png
diff --git a/ch02/images/02_04.png b/ch02/images/02_04.png
diff --git a/ch02/images/02_05.png b/ch02/images/02_05.png
diff --git a/ch02/images/02_06.png b/ch02/images/02_06.png
diff --git a/ch02/images/02_07.png b/ch02/images/02_07.png
diff --git a/ch02/images/02_08.png b/ch02/images/02_08.png
diff --git a/ch02/images/02_09.png b/ch02/images/02_09.png
diff --git a/ch02/images/02_10.png b/ch02/images/02_10.png
diff --git a/ch02/images/02_11.png b/ch02/images/02_11.png
diff --git a/ch02/images/02_12.png b/ch02/images/02_12.png
diff --git a/ch02/images/02_13.png b/ch02/images/02_13.png
diff --git a/ch02/images/02_14.png b/ch02/images/02_14.png
diff --git a/ch02/images/02_14_1.png b/ch02/images/02_14_1.png
diff --git a/ch02/images/02_14_2.png b/ch02/images/02_14_2.png
diff --git a/ch02/images/02_15.png b/ch02/images/02_15.png
diff --git a/ch02/images/02_15_1.png b/ch02/images/02_15_1.png
diff --git a/ch02/images/02_15_2.png b/ch02/images/02_15_2.png
diff --git a/ch02/images/3547_02_images.pdf b/ch02/images/3547_02_images.pdf
diff --git a/ch02/images/3547_02_images.pptx b/ch02/images/3547_02_images.pptx
diff --git a/ch02/images/_02_15.png b/ch02/images/_02_15.png
diff --git a/ch02/images/_02_16.png b/ch02/images/_02_16.png
diff --git a/ch02/images/jupyter-example-1.png b/ch02/images/jupyter-example-1.png
diff --git a/ch02/images/jupyter-example-2.png b/ch02/images/jupyter-example-2.png
diff --git a/ch02/iris.data b/ch02/iris.data
@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+
diff --git a/ch02/iris.names.txt b/ch02/iris.names.txt
@@ -0,0 +1,69 @@
+1. Title: Iris Plants Database
+	Updated Sept 21 by C.Blake - Added discrepency information
+
+2. Sources:
+     (a) Creator: R.A. Fisher
+     (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+     (c) Date: July, 1988
+
+3. Past Usage:
+   - Publications: too many to mention!!!  Here are a few.
+   1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
+      Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
+      to Mathematical Statistics" (John Wiley, NY, 1950).
+   2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
+      (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
+   3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
+      Structure and Classification Rule for Recognition in Partially Exposed
+      Environments".  IEEE Transactions on Pattern Analysis and Machine
+      Intelligence, Vol. PAMI-2, No. 1, 67-71.
+      -- Results:
+         -- very low misclassification rates (0% for the setosa class)
+   4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE 
+      Transactions on Information Theory, May 1972, 431-433.
+      -- Results:
+         -- very low misclassification rates again
+   5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
+      conceptual clustering system finds 3 classes in the data.
+
+4. Relevant Information:
+   --- This is perhaps the best known database to be found in the pattern
+       recognition literature.  Fisher's paper is a classic in the field
+       and is referenced frequently to this day.  (See Duda & Hart, for
+       example.)  The data set contains 3 classes of 50 instances each,
+       where each class refers to a type of iris plant.  One class is
+       linearly separable from the other 2; the latter are NOT linearly
+       separable from each other.
+   --- Predicted attribute: class of iris plant.
+   --- This is an exceedingly simple domain.
+   --- This data differs from the data presented in Fishers article
+	(identified by Steve Chadwick,  spchadwick@espeedaz.net )
+	The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
+	where the error is in the fourth feature.
+	The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
+	where the errors are in the second and third features.  
+
+5. Number of Instances: 150 (50 in each of three classes)
+
+6. Number of Attributes: 4 numeric, predictive attributes and the class
+
+7. Attribute Information:
+   1. sepal length in cm
+   2. sepal width in cm
+   3. petal length in cm
+   4. petal width in cm
+   5. class: 
+      -- Iris Setosa
+      -- Iris Versicolour
+      -- Iris Virginica
+
+8. Missing Attribute Values: None
+
+Summary Statistics:
+	         Min  Max   Mean    SD   Class Correlation
+   sepal length: 4.3  7.9   5.84  0.83    0.7826   
+    sepal width: 2.0  4.4   3.05  0.43   -0.4194
+   petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
+    petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)
+
+9. Class Distribution: 33.3% for each of 3 classes.
diff --git a/ch05/README.md b/ch05/README.md
@@ -0,0 +1,57 @@
+Python Machine Learning - Code Examples
+
+
+##  Chapter 5: Compressing Data via Dimensionality Reduction
+
+### Chapter Outline
+
+- Unsupervised dimensionality reduction via principal component analysis
+  - The main steps behind principal component analysis
+  - Extracting the principal components step by step
+  - Total and explained variance
+  - Feature transformation
+  - Principal component analysis in scikit-learn
+- Supervised data compression via linear discriminant analysis
+  - Principal component analysis versus linear discriminant analysis
+  - The inner workings of linear discriminant analysis
+  - Computing the scatter matrices
+  - Selecting linear discriminants for the new feature subspace
+  - Projecting samples onto the new feature space
+  - LDA via scikit-learn
+- Using kernel principal component analysis for nonlinear mappings
+  - Kernel functions and the kernel trick
+  - Implementing a kernel principal component analysis in Python
+    - Example 1 – separating half-moon shapes
+    - Example 2 – separating concentric circles
+  - Projecting new data points
+  - Kernel principal component analysis in scikit-learn
+- Summary
+
+### A note on using the code examples
+
+The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
+
+![](../ch02/images/jupyter-example-1.png)
+
+
+
+Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
+
+    conda install jupyter notebook
+
+Then you can launch jupyter notebook by executing
+
+    jupyter notebook
+
+A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
+
+**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
+
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch05.ipynb`](ch05.ipynb))**
+
+In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
+
+![](../ch02/images/jupyter-example-2.png)
+
+
+When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor. 
diff --git a/ch05/ch05.ipynb b/ch05/ch05.ipynb
diff --git a/ch05/ch05.py b/ch05/ch05.py
diff --git a/ch05/images/05_01.png b/ch05/images/05_01.png
diff --git a/ch05/images/05_02.png b/ch05/images/05_02.png
diff --git a/ch05/images/05_03.png b/ch05/images/05_03.png
diff --git a/ch05/images/05_04.png b/ch05/images/05_04.png
diff --git a/ch05/images/05_05.png b/ch05/images/05_05.png
diff --git a/ch05/images/05_06.png b/ch05/images/05_06.png
diff --git a/ch05/images/05_07.png b/ch05/images/05_07.png
diff --git a/ch05/images/05_08.png b/ch05/images/05_08.png
diff --git a/ch05/images/05_09.png b/ch05/images/05_09.png
diff --git a/ch05/images/05_10.png b/ch05/images/05_10.png
diff --git a/ch05/images/05_11.png b/ch05/images/05_11.png
diff --git a/ch05/images/05_12.png b/ch05/images/05_12.png
diff --git a/ch05/images/05_13.png b/ch05/images/05_13.png
diff --git a/ch05/images/05_14.png b/ch05/images/05_14.png
diff --git a/ch05/images/05_15.png b/ch05/images/05_15.png
diff --git a/ch05/images/05_16.png b/ch05/images/05_16.png
diff --git a/ch05/images/05_17.png b/ch05/images/05_17.png
diff --git a/ch05/images/05_18.png b/ch05/images/05_18.png
diff --git a/ch05/images/05_19.png b/ch05/images/05_19.png
diff --git a/ch05/wine.data b/ch05/wine.data
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diff --git a/ch05/wine.names.txt b/ch05/wine.names.txt
@@ -0,0 +1,100 @@
+1. Title of Database: Wine recognition data
+	Updated Sept 21, 1998 by C.Blake : Added attribute information
+
+2. Sources:
+   (a) Forina, M. et al, PARVUS - An Extendible Package for Data
+       Exploration, Classification and Correlation. Institute of Pharmaceutical
+       and Food Analysis and Technologies, Via Brigata Salerno, 
+       16147 Genoa, Italy.
+
+   (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
+   (c) July 1991
+3. Past Usage:
+
+   (1)
+   S. Aeberhard, D. Coomans and O. de Vel,
+   Comparison of Classifiers in High Dimensional Settings,
+   Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
+   Mathematics and Statistics, James Cook University of North Queensland.
+   (Also submitted to Technometrics).
+
+   The data was used with many others for comparing various 
+   classifiers. The classes are separable, though only RDA 
+   has achieved 100% correct classification.
+   (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
+   (All results using the leave-one-out technique)
+
+   In a classification context, this is a well posed problem 
+   with "well behaved" class structures. A good data set 
+   for first testing of a new classifier, but not very 
+   challenging.
+
+   (2) 
+   S. Aeberhard, D. Coomans and O. de Vel,
+   "THE CLASSIFICATION PERFORMANCE OF RDA"
+   Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
+   Mathematics and Statistics, James Cook University of North Queensland.
+   (Also submitted to Journal of Chemometrics).
+
+   Here, the data was used to illustrate the superior performance of
+   the use of a new appreciation function with RDA. 
+
+4. Relevant Information:
+
+   -- These data are the results of a chemical analysis of
+      wines grown in the same region in Italy but derived from three
+      different cultivars.
+      The analysis determined the quantities of 13 constituents
+      found in each of the three types of wines. 
+
+   -- I think that the initial data set had around 30 variables, but 
+      for some reason I only have the 13 dimensional version. 
+      I had a list of what the 30 or so variables were, but a.) 
+      I lost it, and b.), I would not know which 13 variables
+      are included in the set.
+
+   -- The attributes are (dontated by Riccardo Leardi, 
+	riclea@anchem.unige.it )
+ 	1) Alcohol
+ 	2) Malic acid
+ 	3) Ash
+	4) Alcalinity of ash  
+ 	5) Magnesium
+	6) Total phenols
+ 	7) Flavanoids
+ 	8) Nonflavanoid phenols
+ 	9) Proanthocyanins
+	10)Color intensity
+ 	11)Hue
+ 	12)OD280/OD315 of diluted wines
+ 	13)Proline            
+
+5. Number of Instances
+
+      	class 1 59
+	class 2 71
+	class 3 48
+
+6. Number of Attributes 
+
+	13
+
+7. For Each Attribute:
+
+	All attributes are continuous
+
+	No statistics available, but suggest to standardise
+	variables for certain uses (e.g. for us with classifiers
+	which are NOT scale invariant)
+
+	NOTE: 1st attribute is class identifier (1-3)
+
+8. Missing Attribute Values:
+
+	None
+
+9. Class Distribution: number of instances per class
+
+      	class 1 59
+	class 2 71
+	class 3 48