First try new cutPoints algorithm

Makefile

@@ -24,7 +24,9 @@ buildext:  ## Build extension
 	rm -fr build/*
 	make clean
 	python setup.py build_ext
-	echo "Build extension success"; mv build/lib.macosx-12-x86_64-cpython-310/cppfimdlp.cpython-310-darwin.so fimdlp;
+	echo "Build extension success"
+	if [ -f build/lib.macosx-12-x86_64-cpython-310/cppfimdlp.cpython-310-darwin.so ] ; then mv build/lib.macosx-12-x86_64-cpython-310/cppfimdlp.cpython-310-darwin.so fimdlp; fi
+	if [ -f build/lib.macosx-10.9-universal2-3.10/cppfimdlp.cpython-310-darwin.so ] ; then mv build/lib.macosx-10.9-universal2-3.10/cppfimdlp.cpython-310-darwin.so fimdlp; fi
 
 audit: ## Audit pip
 	pip-audit

fimdlp/CPPFImdlp.cpp

@@ -18,6 +18,55 @@ namespace CPPFImdlp
     {
     }
     std::vector<float> CPPFImdlp::cutPoints(std::vector<float> &X, std::vector<int> &y)
+    {
+        std::vector<float> cutPts;
+        std::vector<size_t> cutIdx;
+        float xPrev, cutPoint, curx;
+        int yPrev, cury;
+        size_t idxPrev, idx;
+        bool first = true;
+        std::vector<size_t> indices = sortIndices(X);
+        xPrev = X.at(indices.at(0));
+        yPrev = y.at(indices.at(0));
+        idxPrev = indices.at(0);
+        idx = 0;
+        while (idx < indices.size() - 1)
+        {
+            if (first)
+            {
+                first = false;
+                curx = X.at(indices.at(idx));
+                cury = y.at(indices.at(idx));
+            }
+            if (debug)
+                printf("<idx=%lu -> (%3.1f, %d) Prev(%3.1f, %d)\n", idx, curx, cury, xPrev, yPrev);
+            // Read the same values and check class changes
+            while (idx < indices.size() - 1 && curx == xPrev)
+            {
+                idx++;
+                curx = X.at(indices.at(idx));
+                cury = y.at(indices.at(idx));
+                if (cury != yPrev && curx == xPrev)
+                {
+                    yPrev = -1;
+                }
+                if (debug)
+                    printf(">idx=%lu -> (%3.1f, %d) Prev(%3.1f, %d)\n", idx, curx, cury, xPrev, yPrev);
+            }
+            if (yPrev == -1 || yPrev != cury)
+            {
+                cutPoint = (xPrev + curx) / 2;
+                printf("Cutpoint (%3.1f, %d) -> (%3.1f, %d) = %3.1f", xPrev, yPrev, curx, cury, cutPoint);
+                cutPts.push_back(cutPoint);
+                cutIdx.push_back(idxPrev);
+            }
+            yPrev = cury;
+            xPrev = curx;
+            idxPrev = indices.at(idx);
+        }
+        return cutPts;
+    }
+    std::vector<float> CPPFImdlp::cutPointsAnt(std::vector<float> &X, std::vector<int> &y)
     {
         std::vector<float> cutPts;
         std::vector<int> cutIdx;

fimdlp/CPPFImdlp.h

@@ -19,6 +19,7 @@ namespace CPPFImdlp
         CPPFImdlp(int, bool debug = false);
         ~CPPFImdlp();
         std::vector<float> cutPoints(std::vector<float> &, std::vector<int> &);
+        std::vector<float> cutPointsAnt(std::vector<float> &, std::vector<int> &);
     };
 }
 #endif

fimdlp/cfimdlp.pyx

@@ -8,6 +8,7 @@ cdef extern from "CPPFImdlp.h" namespace "CPPFImdlp":
         CPPFImdlp() except + 
         CPPFImdlp(int, bool) except + 
         vector[float] cutPoints(vector[float]&, vector[int]&)
+        vector[float] cutPointsAnt(vector[float]&, vector[int]&)
 
 cdef class CFImdlp:
     cdef CPPFImdlp *thisptr
@@ -17,3 +18,5 @@ cdef class CFImdlp:
         del self.thisptr
     def cut_points(self, X, y):
         return self.thisptr.cutPoints(X, y)
+    def cut_points_ant(self, X, y):
+        return self.thisptr.cutPointsAnt(X, y)

fimdlp/mdlp.py

@@ -61,7 +61,7 @@ class FImdlp(TransformerMixin, BaseEstimator):
         self.n_features_ = X.shape[1]
         self.X_ = X
         self.y_ = y
-        self.discretizer_ = CFImdlp()
+        self.discretizer_ = CFImdlp(debug=True)
         return self
 
     def transform(self, X):
@@ -108,5 +108,7 @@ class FImdlp(TransformerMixin, BaseEstimator):
             datax = self.X_[np.argsort(self.X_[:, i]), i]
             y_ = self.y_[np.argsort(self.X_[:, i])]
             Xcutpoints = self.discretizer_.cut_points(datax, y_)
-            print(f"{self.features_[i]:20s}: {Xcutpoints}")
+            print(f"New:{self.features_[i]:20s}: {Xcutpoints}")
+            Xcutpoints = self.discretizer_.cut_points_ant(datax, y_)
+            print(f"Ant:{self.features_[i]:20s}: {Xcutpoints}")
         return X

sample.py

@@ -1,10 +1,22 @@
 from sklearn.datasets import load_iris
 from fimdlp.mdlp import FImdlp
 from fimdlp.cppfimdlp import CFImdlp
+import numpy as np
 
 data = load_iris()
 X = data.data
 y = data.target
 features = data.feature_names
 test = FImdlp()
-test.fit(X, y, features=features).transform(X)
+# test.fit(X, y, features=features).transform(X)
+
+X = np.array(
+    [
+        [5.1, 3.5, 1.4, 0.2],
+        [5.2, 3.0, 1.4, 0.2],
+        [5.3, 3.2, 1.3, 0.2],
+        [5.3, 3.1, 1.5, 0.2],
+    ]
+)
+y = np.array([0, 0, 0, 1])
+test.fit(X, y).transform(X)