Added Cythonized interface for TSNE.

pytsne.build

@@ -0,0 +1,6 @@
+#!/bin/sh
+cython-3.6 pytsne.pyx
+clang++ -shared -fPIC pytsne.c tsne.cpp sptree.cpp \
+  -L/opt/local/lib `python3.6-config --includes --ldflags` \
+  -I/opt/local/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy/core/include \
+  -o pytsne.so

pytsne.pxd

@@ -0,0 +1,6 @@
+cdef extern:
+    ctypedef double double_t
+    void tsne_run(double* X, int N, int D, double* Y,
+      int no_dims, double perplexity, double theta, int rand_seed,
+      int skip_random_init, int max_iter, int stop_lying_iter,
+      int mom_switch_iter) nogil

pytsne.pyx

@@ -0,0 +1,24 @@
+cimport pytsne
+cimport numpy as np
+
+
+cdef class tsne:
+    @staticmethod
+    def run(np.ndarray[double_t, ndim=2, mode="c"] X,
+        int no_dims, # number of output dims
+        double perplexity,
+        double theta,
+        int rand_seed,
+        int skip_random_init,
+        int max_iter=1000,
+        int stop_lying_iter=250,
+        int mom_switch_iter=250):
+
+        N = X.shape[0]
+        D = X.shape[1]
+        cdef np.ndarray[double_t, ndim=2, mode="c"] Y = np.ndarray((N, no_dims))
+
+        tsne_run(&X[0, 0], N, D, &Y[0, 0], no_dims, perplexity, theta, rand_seed,
+            skip_random_init, max_iter, stop_lying_iter, mom_switch_iter)
+
+        return Y

pytsne_test.py

@@ -0,0 +1,5 @@
+from pytsne import tsne
+import numpy as np
+
+a = np.random.random((1000,5))
+tsne.run(a, 2, 5, 0, 0, 0)

tsne.cpp

@@ -96,6 +96,7 @@ void TSNE::run(double* X, int N, int D, double* Y, int no_dims, double perplexit
         P = (double*) malloc(N * N * sizeof(double));
         if(P == NULL) { printf("Memory allocation failed!\n"); exit(1); }
         computeGaussianPerplexity(X, N, D, P, perplexity);
+        printf("perplexity: %f\n", perplexity);
 
         // Symmetrize input similarities
         printf("Symmetrizing...\n");
@@ -702,3 +703,15 @@ void TSNE::save_data(double* data, int* landmarks, double* costs, int n, int d)
     fclose(h);
 	printf("Wrote the %i x %i data matrix successfully!\n", n, d);
 }
+
+extern "C" {
+  void tsne_run(double* X, int N, int D, double* Y,
+      int no_dims, double perplexity, double theta,
+      int rand_seed, int skip_random_init,
+      int max_iter, int stop_lying_iter, int mom_switch_iter) {
+
+        TSNE::run(X, N, D, Y, no_dims, perplexity,
+          theta, rand_seed, (bool) skip_random_init,
+          max_iter, stop_lying_iter, mom_switch_iter);
+      }
+}

tsne.h

@@ -41,23 +41,24 @@ static inline double sign(double x) { return (x == .0 ? .0 : (x < .0 ? -1.0 : 1.
 class TSNE
 {
 public:
-    void run(double* X, int N, int D, double* Y, int no_dims, double perplexity, double theta, int rand_seed,
+    static void run(double* X, int N, int D, double* Y, int no_dims, double perplexity, double theta, int rand_seed,
              bool skip_random_init, int max_iter=1000, int stop_lying_iter=250, int mom_switch_iter=250);
-    bool load_data(double** data, int* n, int* d, int* no_dims, double* theta, double* perplexity, int* rand_seed, int* max_iter);
-    void save_data(double* data, int* landmarks, double* costs, int n, int d);
-    void symmetrizeMatrix(unsigned int** row_P, unsigned int** col_P, double** val_P, int N); // should be static!
+    static bool load_data(double** data, int* n, int* d, int* no_dims, double* theta, double* perplexity, int* rand_seed, int* max_iter);
+    static void save_data(double* data, int* landmarks, double* costs, int n, int d);
+    static void symmetrizeMatrix(unsigned int** row_P, unsigned int** col_P, double** val_P, int N); // should be static!
 
 
 private:
-    void computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta);
-    void computeExactGradient(double* P, double* Y, int N, int D, double* dC);
-    double evaluateError(double* P, double* Y, int N, int D);
-    double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta);
-    void zeroMean(double* X, int N, int D);
-    void computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity);
-    void computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K);
-    void computeSquaredEuclideanDistance(double* X, int N, int D, double* DD);
-    double randn();
+    static void computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta);
+    static void computeExactGradient(double* P, double* Y, int N, int D, double* dC);
+    static double evaluateError(double* P, double* Y, int N, int D);
+    static double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta);
+    static void zeroMean(double* X, int N, int D);
+    static void computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity);
+    static void computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K);
+    static void computeSquaredEuclideanDistance(double* X, int N, int D, double* DD);
+    static double randn();
 };
 
+
 #endif