Add dynamic class name and update examples

examples/estimator/classifier/KNeighborsClassifier/java/basics.py

@@ -21,7 +21,7 @@
 """
 import java.util.*;
 
-class Brain {
+class KNeighborsClassifier {
 
     private int nNeighbors;
     private int nTemplates;
@@ -30,7 +30,7 @@ class Brain {
     private double[][] X;
     private int[] y;
 
-    public Brain(int nNeighbors, int nTemplates, int nClasses, double power, double[][] X, int[] y) {
+    public KNeighborsClassifier(int nNeighbors, int nTemplates, int nClasses, double power, double[][] X, int[] y) {
         this.nNeighbors = nNeighbors;
         this.nTemplates = nTemplates;
         this.nClasses = nClasses;
@@ -48,7 +48,7 @@ class Brain {
         }
     }
 
-    private static double compDist(double[] temp, double[] cand, double q) {
+    private static double compute(double[] temp, double[] cand, double q) {
         double dist = 0.;
         double diff;
         for (int i = 0, l = temp.length; i < l; i++) {
@@ -80,7 +80,7 @@ class Brain {
             double minDist = Double.POSITIVE_INFINITY;
             double curDist;
             for (int i = 0; i < this.nTemplates; i++) {
-                curDist = Brain.compDist(this.X[i], features, this.power);
+                curDist = KNeighborsClassifier.compute(this.X[i], features, this.power);
                 if (curDist <= minDist) {
                     minDist = curDist;
                     classIdx = y[i];
@@ -90,7 +90,7 @@ class Brain {
             int[] classes = new int[this.nClasses];
             ArrayList<Neighbor> dists = new ArrayList<Neighbor>();
             for (int i = 0; i < this.nTemplates; i++) {
-                dists.add(new Neighbor(y[i], Brain.compDist(this.X[i], features, this.power)));
+                dists.add(new Neighbor(y[i], KNeighborsClassifier.compute(this.X[i], features, this.power)));
             }
             Collections.sort(dists, new Comparator<Neighbor>() {
                 @Override
@@ -126,7 +126,7 @@ class Brain {
             int[] y = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2};
 
             // Prediction:
-            Brain clf = new Brain(3, 150, 3, 2, X, y);
+            KNeighborsClassifier clf = new KNeighborsClassifier(3, 150, 3, 2, X, y);
             int estimation = clf.predict(features);
             System.out.println(estimation);
 

examples/estimator/classifier/KNeighborsClassifier/js/basics.py

@@ -19,7 +19,7 @@
 print(output)
 
 """
-var Brain = function(nNeighbors, nTemplates, nClasses, power, X, y) {
+var KNeighborsClassifier = function(nNeighbors, nTemplates, nClasses, power, X, y) {
 
     this.nNeighbors = nNeighbors;
     this.nTemplates = nTemplates;
@@ -107,7 +107,7 @@
         var y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2];
 
         // Estimator:
-        var clf = new Brain(3, 150, 3, 2, X, y);
+        var clf = new KNeighborsClassifier(3, 150, 3, 2, X, y);
         var prediction = clf.predict(features);
         console.log(prediction);
 

sklearn_porter/Porter.py

@@ -147,7 +147,7 @@ def __init__(self, estimator, language='java', method='predict', **kwargs):
         # Create instance with all parameters:
         self.template = clazz(**self.__dict__)
 
-    def export(self, class_name='Brain', method_name='predict',
+    def export(self, class_name=None, method_name=None,
                use_repr=True, details=False, **kwargs):
         # pylint: disable=unused-argument
         """
@@ -156,10 +156,10 @@ def export(self, class_name='Brain', method_name='predict',
 
         Parameters
         ----------
-        :param class_name : string, default: 'Brain'
+        :param class_name : string, default: None
             The name for the ported class.
 
-        :param method_name : string, default: 'predict'
+        :param method_name : string, default: None
             The name for the ported method.
 
         :param use_repr : bool, default: True
@@ -175,6 +175,13 @@ def export(self, class_name='Brain', method_name='predict',
             The ported model as string or a dictionary
             with further information.
         """
+
+        if class_name is None:
+            class_name = self.estimator_name
+
+        if method_name is None:
+            method_name = self.target_method
+
         output = self.template.export(class_name=class_name,
                                       method_name=method_name,
                                       use_repr=use_repr)

sklearn_porter/estimator/classifier/AdaBoostClassifier/__init__.py

@@ -94,15 +94,15 @@ def __init__(self, estimator, target_language='java',
                 self.n_estimators += 1
                 self.n_features = self.estimator.estimators_[idx].n_features_
 
-    def export(self, class_name="Brain", method_name="predict", use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 
         Parameters
         ----------
-        :param class_name: string, default: 'Brain'
+        :param class_name: string
             The name of the class in the returned result.
-        :param method_name: string, default: 'predict'
+        :param method_name: string
             The name of the method in the returned result.
         :param use_repr : bool, default True
             Whether to use repr() for floating-point values or not.

sklearn_porter/estimator/classifier/BernoulliNB/__init__.py

@@ -91,15 +91,15 @@ def __init__(self, estimator, target_language='java',
         self.del_probs = temp_arr__.format(type='double', name='delProbs',
                                            values=probs)
 
-    def export(self, class_name="Brain", method_name="predict", use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 
         Parameters
         ----------
-        :param class_name: string, default: 'Brain'
+        :param class_name: string
             The name of the class in the returned result.
-        :param method_name: string, default: 'predict'
+        :param method_name: string
             The name of the method in the returned result.
         :param use_repr : bool, default True
             Whether to use repr() for floating-point values or not.

sklearn_porter/estimator/classifier/DecisionTreeClassifier/__init__.py

@@ -88,15 +88,15 @@ def __init__(self, estimator, target_language='java',
         self.n_features = estimator.n_features_
         self.n_classes = estimator.n_classes_
 
-    def export(self, class_name="Brain", method_name="predict", use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 
         Parameters
         ----------
-        :param class_name: string, default: 'Brain'
+        :param class_name: string
             The name of the class in the returned result.
-        :param method_name: string, default: 'predict'
+        :param method_name: string
             The name of the method in the returned result.
         :param use_repr : bool, default True
             Whether to use repr() for floating-point values or not.

sklearn_porter/estimator/classifier/GaussianNB/__init__.py

@@ -87,15 +87,15 @@ def __init__(self, estimator, target_language='java',
         self.thetas = temp_arr__.format(type='double', name='thetas',
                                         values=thetas)
 
-    def export(self, class_name="Brain", method_name="predict", use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 
         Parameters
         ----------
-        :param class_name: string, default: 'Brain'
+        :param class_name: string
             The name of the class in the returned result.
-        :param method_name: string, default: 'predict'
+        :param method_name: string
             The name of the method in the returned result.
         :param use_repr : bool, default True
             Whether to use repr() for floating-point values or not.

sklearn_porter/estimator/classifier/KNeighborsClassifier/__init__.py

@@ -72,7 +72,7 @@ def __init__(self, estimator, target_language='java',
             msg = "Only 'uniform' weights are supported for this classifier."
             raise NotImplementedError(msg)
 
-    def export(self, class_name="Brain", method_name="predict", use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 

sklearn_porter/estimator/classifier/LinearSVC/__init__.py

@@ -89,7 +89,7 @@ def __init__(self, estimator, target_language='java',
         self.is_binary = self.n_classes == 2
         self.prefix = 'binary' if self.is_binary else 'multi'
 
-    def export(self, class_name="Brain", method_name="predict", use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 

sklearn_porter/estimator/classifier/MLPClassifier/__init__.py

@@ -105,15 +105,15 @@ def output_activation_functions(self):
         """Get list of supported activation functions for the output layer."""
         return ['softmax', 'logistic']
 
-    def export(self, class_name='Brain', method_name='predict', use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 
         Parameters
         ----------
-        :param class_name: string, default: 'Brain'
+        :param class_name: string
             The name of the class in the returned result.
-        :param method_name: string, default: 'predict'
+        :param method_name: string
             The name of the method in the returned result.
         :param use_repr : bool, default True
             Whether to use repr() for floating-point values or not.

sklearn_porter/estimator/classifier/RandomForestClassifier/__init__.py

@@ -98,15 +98,15 @@ def __init__(self, estimator, target_language='java',
             self.n_estimators += 1
             self.n_features = self.estimator.estimators_[idx].n_features_
 
-    def export(self, class_name="Brain", method_name="predict", use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 
         Parameters
         ----------
-        :param class_name: string, default: 'Brain'
+        :param class_name: string
             The name of the class in the returned result.
-        :param method_name: string, default: 'predict'
+        :param method_name: string
             The name of the method in the returned result.
         :param use_repr : bool, default True
             Whether to use repr() for floating-point values or not.

sklearn_porter/estimator/classifier/SVC/__init__.py

@@ -97,8 +97,7 @@ def __init__(self, estimator, target_language='java',
         self.is_binary = self.n_classes == 2
         self.prefix = 'binary' if self.is_binary else 'multi'
 
-    def export(self, class_name="Brain", method_name="predict",
-               use_repr=True, use_file=False):
+    def export(self, class_name, method_name, use_repr=True, use_file=False):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 

sklearn_porter/estimator/regressor/MLPRegressor/__init__.py

@@ -81,15 +81,15 @@ def hidden_activation_functions(self):
         """Get list of supported activation functions for the hidden layers."""
         return ['relu', 'identity', 'tanh', 'logistic']
 
-    def export(self, class_name='Brain', method_name='predict', use_repr=True):
+    def export(self, class_name, method_name, use_repr=True):
         """
         Port a trained estimator to the syntax of a chosen programming language.
 
         Parameters
         ----------
-        :param class_name: string, default: 'Brain'
+        :param class_name: string
             The name of the class in the returned result.
-        :param method_name: string, default: 'predict'
+        :param method_name: string
             The name of the method in the returned result.
         :param use_repr : bool, default True
             Whether to use repr() for floating-point values or not.