Restructure model data and algorithm handling

sklearn_porter/estimator/classifier/KNeighborsClassifier/__init__.py

@@ -127,6 +127,27 @@ def create_method(self):
  temp_arr_ = self.temp('arr[]')
  temp_arr__ = self.temp('arr[][]')
 
+ temp_method = self.temp('method.predict', n_indents=1, skipping=True)
+ out = temp_method.format(class_name=self.class_name,
+ method_name=self.method_name,
+ distance_computation=distance_comp)
+ return out
+
+ def create_class(self, method):
+ """
+ Build the estimator class.
+
+ Returns
+ -------
+ :return out : string
+ The built class as string.
+ """
+
+ temp_type = self.temp('type')
+ temp_arr = self.temp('arr')
+ temp_arr_ = self.temp('arr[]')
+ temp_arr__ = self.temp('arr[][]')
+
  # Templates
  temps = []
  for atts in enumerate(self.estimator._fit_X): # pylint: disable=W0212
@@ -144,28 +165,12 @@ def create_method(self):
  classes = temp_arr_.format(type='int', name='y', values=classes,
  n=self.n_templates)
 
- temp_method = self.temp('method.predict', n_indents=1, skipping=True)
- out = temp_method.format(class_name=self.class_name,
- method_name=self.method_name,
- n_neighbors=self.n_neighbors,
- n_templates=self.n_templates,
- n_features=self.n_features,
- n_classes=self.n_classes,
- distance_computation=distance_comp,
- power=self.power_param, X=temps, y=classes)
- return out
-
- def create_class(self, method):
- """
- Build the estimator class.
-
- Returns
- -------
- :return out : string
- The built class as string.
- """
  temp_class = self.temp('class')
  out = temp_class.format(class_name=self.class_name,
  method_name=self.method_name, method=method,
- n_features=self.n_features)
+ n_features=self.n_features, X=temps, y=classes,
+ n_neighbors=self.n_neighbors,
+ n_templates=self.n_templates,
+ n_classes=self.n_classes,
+ power=self.power_param)
  return out

sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/java/class.txt

@@ -2,6 +2,22 @@ import java.util.*;
 
 class {class_name} {{
 
+ private int nNeighbors;
+ private int nTemplates;
+ private int nClasses;
+ private double power;
+ private double[][] X;
+ private int[] y;
+
+ public {class_name}(int nNeighbors, int nTemplates, int nClasses, double power, double[][] X, int[] y) {{
+ this.nNeighbors = nNeighbors;
+ this.nTemplates = nTemplates;
+ this.nClasses = nClasses;
+ this.power = power;
+ this.X = X;
+ this.y = y;
+ }}
+
  private static class Neighbor {{
  Integer clazz;
  Double dist;
@@ -15,11 +31,23 @@ class {class_name} {{
 
  public static void main(String[] args) {{
  if (args.length == {n_features}) {{
- double[] atts = new double[args.length];
+
+ // Features:
+ double[] features = new double[args.length];
  for (int i = 0, l = args.length; i < l; i++) {{
- atts[i] = Double.parseDouble(args[i]);
+ features[i] = Double.parseDouble(args[i]);
  }}
- System.out.println({class_name}.{method_name}(atts));
+
+ // Parameters:
+ {X}
+ {y}
+
+ // Prediction:
+ {class_name} clf = new {class_name}({n_neighbors}, {n_templates}, {n_classes}, {power}, X, y);
+ int estimation = clf.{method_name}(features);
+ System.out.println(estimation);
+
  }}
  }}
+
 }}

sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/java/method.predict.txt

@@ -1,46 +1,34 @@
 {distance_computation}
 
-public static int {method_name}(double[] atts) {{
- if (atts.length != {n_features}) {{
- return -1;
- }}
-
- {X}
- {y}
-
+public int {method_name}(double[] features) {{
  int classIdx = -1;
- int nNeighbors = {n_neighbors};
- int nTemplates = {n_templates};
- int nClasses = {n_classes};
- double power = {power};
-
- if (nNeighbors == 1) {{
+ if (this.nNeighbors == 1) {{
  double minDist = Double.POSITIVE_INFINITY;
  double curDist;
- for (int i = 0; i < nTemplates; i++) {{
- curDist = {class_name}.compDist(X[i], atts, power);
+ for (int i = 0; i < this.nTemplates; i++) {{
+ curDist = {class_name}.compute(this.X[i], features, this.power);
  if (curDist <= minDist) {{
  minDist = curDist;
  classIdx = y[i];
  }}
  }}
  }} else {{
- int[] classes = new int[nClasses];
+ int[] classes = new int[this.nClasses];
  ArrayList<Neighbor> dists = new ArrayList<Neighbor>();
- for (int i = 0; i < nTemplates; i++) {{
- dists.add(new Neighbor(y[i], {class_name}.compDist(X[i], atts, power)));
+ for (int i = 0; i < this.nTemplates; i++) {{
+ dists.add(new Neighbor(y[i], {class_name}.compute(this.X[i], features, this.power)));
  }}
  Collections.sort(dists, new Comparator<Neighbor>() {{
  @Override
  public int compare(Neighbor n1, Neighbor n2) {{
  return n1.dist.compareTo(n2.dist);
  }}
  }});
- for (Neighbor neighbor : dists.subList(0, nNeighbors)) {{
+ for (Neighbor neighbor : dists.subList(0, this.nNeighbors)) {{
  classes[neighbor.clazz]++;
  }}
  int classVal = -1;
- for (int i = 0; i < nClasses; i++) {{
+ for (int i = 0; i < this.nClasses; i++) {{
  if (classes[i] > classVal) {{
  classVal = classes[i];
  classIdx = i;

sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/java/metric.minkowski.txt

@@ -1,4 +1,4 @@
-public 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++) {

sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/js/class.txt

@@ -1,21 +1,35 @@
-// Array.prototype.fill polyfill:
-[].fill||(Array.prototype.fill=function(a){{for(var b=Object(this),c=parseInt(b.length,10),d=arguments[1],e=parseInt(d,10)||0,f=0>e?Math.max(c+e,0):Math.min(e,c),g=arguments[2],h=void 0===g?c:parseInt(g)||0,i=0>h?Math.max(c+h,0):Math.min(h,c);i>f;f++)b[f]=a;return b}});
+var {class_name} = function(nNeighbors, nTemplates, nClasses, power, X, y) {{
 
-var Neighbor = function(clazz, dist) {{
- this.clazz = clazz;
- this.dist = dist;
-}};
+ this.nNeighbors = nNeighbors;
+ this.nTemplates = nTemplates;
+ this.nClasses = nClasses;
+ this.power = power;
+ this.X = X;
+ this.y = y;
 
-var {class_name} = function() {{
+ var Neighbor = function(clazz, dist) {{
+ this.clazz = clazz;
+ this.dist = dist;
+ }};
 
  {method}
 
 }};
 
 if (typeof process !== 'undefined' && typeof process.argv !== 'undefined') {{
- if (process.argv.length - 2 == {n_features}) {{
- var argv = process.argv.slice(2);
- var prediction = new {class_name}().{method_name}(argv);
+ if (process.argv.length - 2 === {n_features}) {{
+
+ // Features:
+ var features = process.argv.slice(2);
+
+ // Parameters:
+ {X}
+ {y}
+
+ // Estimator:
+ var clf = new {class_name}({n_neighbors}, {n_templates}, {n_classes}, {power}, X, y);
+ var prediction = clf.{method_name}(features);
  console.log(prediction);
+
  }}
 }}

sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/js/method.predict.txt

@@ -1,45 +1,31 @@
 {distance_computation}
 
-this.{method_name} = function(atts) {{
-
- if (atts.length != {n_features}) {{
- return -1;
- }}
-
- {X}
- {y}
-
- var classIdx = -1,
- nNeighbors = {n_neighbors},
- nTemplates = {n_templates},
- nClasses = {n_classes},
- power = {power},
- i;
-
- if (nNeighbors == 1) {{
+this.{method_name} = function(features) {{
+ var classIdx = -1, i;
+ if (this.nNeighbors == 1) {{
  var minDist = Number.POSITIVE_INFINITY,
  curDist;
- for (i = 0; i < nTemplates; i++) {{
- curDist = compDist(X[i], atts, power);
+ for (i = 0; i < this.nTemplates; i++) {{
+ curDist = compute(this.X[i], features, this.power);
  if (curDist <= minDist) {{
  minDist = curDist;
- classIdx = y[i];
+ classIdx = this.y[i];
  }}
  }}
  }} else {{
- var classes = new Array(nClasses).fill(0);
+ var classes = new Array(this.nClasses).fill(0);
  var dists = [];
- for (i = 0; i < nTemplates; i++) {{
- dists.push(new Neighbor(y[i], compDist(X[i], atts, power)));
+ for (i = 0; i < this.nTemplates; i++) {{
+ dists.push(new Neighbor(this.y[i], compute(this.X[i], features, this.power)));
  }}
  dists.sort(function compare(n1, n2) {{
  return (n1.dist < n2.dist) ? -1 : 1;
  }});
- for (i = 0; i < nNeighbors; i++) {{
+ for (i = 0; i < this.nNeighbors; i++) {{
  classes[dists[i].clazz]++;
  }}
  var classVal = -1;
- for (i = 0; i < nClasses; i++) {{
+ for (i = 0; i < this.nClasses; i++) {{
  if (classes[i] > classVal) {{
  classVal = classes[i];
  classIdx = i;

sklearn_porter/estimator/classifier/KNeighborsClassifier/templates/js/metric.minkowski.txt

@@ -1,4 +1,4 @@
-var compDist = function(temp, cand, q) {
+var compute = function(temp, cand, q) {
  var dist = 0.,
  diff;
  for (var i = 0, l = temp.length; i < l; i++) {