Merge pull request #24 from ctralie/sparsedm

Sparsedm

Author: ctralie

Makefile

@@ -1,7 +1,7 @@
 build: ripser
 
 
-all: python ripser ripser-coeff ripser-reduction ripser-debug
+all: ripser ripser-coeff ripser-reduction ripser-coeff-reduction ripser-debug
 
 python: src/ripser.cpp
 	c++ -std=c++11 src/ripser.cpp -c -o ripser -Ofast -D NDEBUG -D PYTHON_EXTENSION
@@ -15,10 +15,12 @@ python: src/ripser.cpp
 # ripser-reduction: ripser.cpp
 # 	c++ -std=c++11 ripser.cpp -o ripser-reduction -Ofast -D NDEBUG -D ASSEMBLE_REDUCTION_MATRIX -D PRINT_PERSISTENCE_PAIRS
 
-# ripser-debug: ripser.cpp
-# 	c++ -std=c++11 ripser.cpp -o ripser-debug -g
+ripser-coeff-reduction: ripser.cpp
+	c++ -std=c++11 ripser.cpp -o ripser-coeff-reduction -Ofast -D NDEBUG -D USE_COEFFICIENTS -D ASSEMBLE_REDUCTION_MATRIX
+
+ripser-debug: ripser.cpp
+	c++ -std=c++11 ripser.cpp -o ripser-debug -g
 
 
 clean:
-	rm -f ripser 
-#ripser-coeff ripser-reduction ripser-debug
+	rm -f ripser ripser-coeff ripser-reduction ripser-coeff-reduction ripser-debug

notebooks/Approximate Sparse Filtrations.ipynb

@@ -1,6 +1,10 @@
 from libcpp.vector cimport vector
 
 cdef extern from "ripser.cpp":
-	vector[float] pythondm(float* D, int N, int modulus, 
-					int dim_max, float threshold, int do_cocycles)
+	vector[float] rips_dm(float* D, int N, int modulus, 
+					     int dim_max, float threshold, int do_cocycles)
 
+cdef extern from "ripser.cpp":
+	vector[float] rips_dm_sparse(int* I, int* J, float* V, int NEdges, 
+							   int N, int modulus, int dim_max, 
+							   float threshold, int do_cocycles)

notebooks/Sparse Distance Matrices.ipynb

@@ -10,6 +10,16 @@ def doRipsFiltrationDM(np.ndarray[float,ndim=1,mode="c"] DParam not None, int ma
 
 	cdef int N = DParam.shape[0]
 
-	res = pyRips.pythondm(&DParam[0], N, coeff, maxHomDim, thresh, do_cocycles)
+	res = pyRips.rips_dm(&DParam[0], N, coeff, maxHomDim, thresh, do_cocycles)
 
 	return res
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def doRipsFiltrationDMSparse(np.ndarray[int,ndim=1,mode="c"] I not None, np.ndarray[int,ndim=1,mode="c"] J not None, np.ndarray[float,ndim=1,mode="c"] V not None, int N, int maxHomDim, float thresh=-1, int coeff=2, int do_cocycles=0):
+
+	cdef int NEdges = I.size
+
+	res = pyRips.rips_dm_sparse(&I[0], &J[0], &V[0], NEdges, N, coeff, maxHomDim, thresh, do_cocycles)
+
+	return res