wine and glass demo dataset docs

README.md

@@ -57,6 +57,8 @@ composer require php-ai/php-ml
     * [CSV](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/csv-dataset)
     * Ready to use:
         * [Iris](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/iris/)
+        * [Wine](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/wine/)
+        * [Glass](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/glass/)
 * Math
     * [Distance](http://php-ml.readthedocs.io/en/latest/math/distance/)
     * [Matrix](http://php-ml.readthedocs.io/en/latest/math/matrix/)

docs/index.md

@@ -57,6 +57,8 @@ composer require php-ai/php-ml
     * [CSV](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/csv-dataset)
     * Ready to use:
         * [Iris](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/iris/)
+        * [Wine](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/wine/)
+        * [Glass](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/glass/)
 * Math
     * [Distance](http://php-ml.readthedocs.io/en/latest/math/distance/)
     * [Matrix](http://php-ml.readthedocs.io/en/latest/math/matrix/)

docs/machine-learning/datasets/demo/glass.md

@@ -0,0 +1,42 @@
+# Glass Dataset
+
+From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)
+
+### Specification
+
+| Classes               | 6     |
+| Samples total         | 214   |
+| Features per sample   | 9     |
+
+Samples per class:
+ * 70 float processed building windows
+ * 17 float processed vehicle windows
+ * 76 non-float processed building windows
+ * 13 containers
+ * 9 tableware
+ * 29 headlamps
+
+### Load
+
+To load Glass dataset simple use:
+
+```
+use Phpml\Dataset\Demo\Glass;
+
+$dataset = new Glass();
+```
+
+### Several samples example
+
+```
+RI: refractive index,Na: Sodium,Mg: Magnesium,Al: Aluminum,Si: Silicon,K: Potassium,Ca: Calcium,Ba: Barium,Fe: Iron,type of glass
+1.52101,13.64,4.49,1.10,71.78,0.06,8.75,0.00,0.00,building_windows_float_processed
+1.51761,13.89,3.60,1.36,72.73,0.48,7.83,0.00,0.00,building_windows_float_processed
+1.51618,13.53,3.55,1.54,72.99,0.39,7.78,0.00,0.00,building_windows_float_processed
+1.51766,13.21,3.69,1.29,72.61,0.57,8.22,0.00,0.00,building_windows_float_processed
+1.51742,13.27,3.62,1.24,73.08,0.55,8.07,0.00,0.00,building_windows_float_processed
+1.51596,12.79,3.61,1.62,72.97,0.64,8.07,0.00,0.26,building_windows_float_processed
+1.51743,13.30,3.60,1.14,73.09,0.58,8.17,0.00,0.00,building_windows_float_processed
+1.51756,13.15,3.61,1.05,73.24,0.57,8.24,0.00,0.00,building_windows_float_processed
+1.51918,14.04,3.58,1.37,72.08,0.56,8.30,0.00,0.00,building_windows_float_processed
+```

docs/machine-learning/datasets/demo/iris.md

@@ -14,6 +14,8 @@ Most popular and widely available dataset of iris flower measurement and class n
 To load Iris dataset simple use:
 
 ```
+use Phpml\Dataset\Demo\Iris;
+
 $dataset = new Iris();
 ```
 

docs/machine-learning/datasets/demo/wine.md

@@ -0,0 +1,35 @@
+# Wine Dataset
+
+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. 
+
+### Specification
+
+| Classes               | 3     |
+| Samples per class     | class 1 59; class 2 71; class 3 48    |
+| Samples total         | 178   |
+| Features per sample   | 13     |
+
+### Load
+
+To load Wine dataset simple use:
+
+```
+use Phpml\Dataset\Demo\Wine;
+
+$dataset = new Wine();
+```
+
+### Several samples example
+
+```
+alcohol,malic acid,ash,alcalinity of ash,magnesium,total phenols,flavanoids,nonflavanoid phenols,proanthocyanins,color intensity,hue,OD280/OD315 of diluted wines,proline,class
+14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065,1
+13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050,1
+13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185,1
+14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480,1
+13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735,1
+14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450,1
+14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290,1
+14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295,1
+14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045,1
+```

docs/machine-learning/preprocessing/imputation-missing-values.md

@@ -0,0 +1 @@
+# Imputation missing values

docs/machine-learning/preprocessing/normalization.md

@@ -0,0 +1 @@
+# Normalization