PyObject *getTanimotoSimMat(python::object bitVectList) {
// we will assume here that we have a either a list of ExplicitBitVectors or
// SparseBitVects
int nrows = python::extract<int>(bitVectList.attr("__len__")());
CHECK_INVARIANT(nrows > 1, "");
// First check what type of vector we have
python::object v1 = bitVectList[0];
python::extract<ExplicitBitVect> ebvWorks(v1);
python::extract<SparseBitVect> sbvWorks(v1);
if(!ebvWorks.check() && !sbvWorks.check()) {
throw_value_error("GetTanimotoDistMat can only take a sequence of ExplicitBitVects or SparseBitvects");
}
npy_intp dMatLen = nrows*(nrows-1)/2;
PyArrayObject *simRes = (PyArrayObject *)PyArray_SimpleNew(1, &dMatLen, NPY_DOUBLE);
double *sMat = (double *)simRes->data;
if (ebvWorks.check()) {
PySequenceHolder<ExplicitBitVect> dData(bitVectList);
MetricMatrixCalc<PySequenceHolder<ExplicitBitVect>, ExplicitBitVect> mmCalc;
mmCalc.setMetricFunc(&TanimotoSimilarityMetric<ExplicitBitVect, ExplicitBitVect>);
mmCalc.calcMetricMatrix(dData, nrows, 0, sMat);
}
else if (sbvWorks.check()) {
PySequenceHolder<SparseBitVect> dData(bitVectList);
MetricMatrixCalc<PySequenceHolder<SparseBitVect>, SparseBitVect> mmCalc;
mmCalc.setMetricFunc(&TanimotoSimilarityMetric<SparseBitVect, SparseBitVect>);
mmCalc.calcMetricMatrix(dData, nrows, 0, sMat);
}
return PyArray_Return(simRes);
}
int main() {
int n = 10;
int m = 3;
int dlen = n * (n - 1) / 2;
int i, j;
double *desc = new double[n * m];
double **desc2D = new double *[n];
for (i = 0; i < n; i++) {
desc2D[i] = desc;
desc += m;
}
desc = desc2D[0];
for (i = 0; i < n; i++) {
for (j = 0; j < m; j++) {
desc[i * m + j] = ((double)rand()) / 10;
}
}
// double x = EuclideanDistanceMetric(desc2D[0], desc2D[1], m);
double *dmat = new double[dlen];
MetricMatrixCalc<double **, double *> mmCalc;
mmCalc.setMetricFunc(&EuclideanDistanceMetric<double *, double *>);
mmCalc.calcMetricMatrix(desc2D, n, m, dmat);
for (i = 0; i < dlen; i++) {
std::cout << dmat[i] << "\n";
}
delete[] desc2D;
delete[] desc;
delete[] dmat;
exit(0);
}
PyObject *getEuclideanDistMat(python::object descripMat) {
// Bit of a pain involved here, we accept three types of PyObjects here
// 1. A Numeric Array
// - first find what 'type' of entry we have (float, double and int is all we recognize for now)
// - then point to contiguous piece of memory from the array that contains the data with a type*
// - then make a new type** pointer so that double index into this contiguous memory will work
// and then pass it along to the distance calculator
// 2. A list of Numeric Vector (or 1D arrays)
// - in this case wrap descripMat with a PySequenceHolder<type*> where type is the
// type of entry in vector (accepted types are int, double and float
// - Then pass the PySequenceHolder to the metrci calculator
// 3. A list (or tuple) of lists (or tuple)
// - In this case other than wrapping descripMat with a PySequenceHolder
// each of the indivual list in there are also wrapped by a PySequenceHolder
// - so the distance calculator is passed in a "PySequenceHolder<PySequenceHolder<double>>"
// - FIX: not that we always convert entry values to double here, even if we passed
// in a list of list of ints (or floats). Given that lists can be heterogeneous, I do not
// know how to ask a list what type of entries if contains.
//
// OK my brain is going to explode now
// first deal with situation where we have an Numeric Array
PyObject *descMatObj = descripMat.ptr();
PyArrayObject *distRes;
if (PyArray_Check(descMatObj)) {
// get the dimensions of the array
int nrows = ((PyArrayObject *)descMatObj)->dimensions[0];
int ncols = ((PyArrayObject *)descMatObj)->dimensions[1];
int i;
CHECK_INVARIANT((nrows > 0) && (ncols > 0), "");
npy_intp dMatLen = nrows*(nrows-1)/2;
// now that we have the dimensions declare the distance matrix which is always a
// 1D double array
distRes = (PyArrayObject *)PyArray_SimpleNew(1, &dMatLen, NPY_DOUBLE);
// grab a pointer to the data in the array so that we can directly put values in there
// and avoid copying :
double *dMat = (double *)distRes->data;
PyArrayObject *copy;
copy = (PyArrayObject *)PyArray_ContiguousFromObject(descMatObj,
((PyArrayObject *)descMatObj)->descr->type_num,
2,2);
// if we have double array
if (((PyArrayObject *)descMatObj)->descr->type_num == NPY_DOUBLE) {
double *desc = (double *)copy->data;
// REVIEW: create an adaptor object to hold a double * and support
// operator[]() so that we don't have to do this stuff:
// here is the 2D array trick this so that when the distance calaculator
// asks for desc2D[i] we basically get the ith row as double*
double **desc2D = new double*[nrows];
for (i = 0; i < nrows; i++) {
desc2D[i] = desc;
desc += ncols;
}
MetricMatrixCalc<double**, double*> mmCalc;
mmCalc.setMetricFunc(&EuclideanDistanceMetric<double *, double *>);
mmCalc.calcMetricMatrix(desc2D, nrows, ncols, dMat);
delete [] desc2D;
// we got the distance matrix we are happy so return
return PyArray_Return(distRes);
}
// if we have a float array
else if (((PyArrayObject *)descMatObj)->descr->type_num == NPY_FLOAT) {
float* desc = (float *)copy->data;
float **desc2D = new float*[nrows];
for (i = 0; i < nrows; i++) {
desc2D[i] = desc;
desc += ncols;
}
MetricMatrixCalc<float**, float*> mmCalc;
mmCalc.setMetricFunc(&EuclideanDistanceMetric<float *, float*>);
mmCalc.calcMetricMatrix(desc2D, nrows, ncols, dMat);
delete [] desc2D;
return PyArray_Return(distRes);
}
// if we have an interger array
else if (((PyArrayObject *)descMatObj)->descr->type_num == NPY_INT) {
int *desc = (int *)copy->data;
int **desc2D = new int*[nrows];
for (i = 0; i < nrows; i++) {
desc2D[i] = desc;
desc += ncols;
}
MetricMatrixCalc<int**, int*> mmCalc;
mmCalc.setMetricFunc(&EuclideanDistanceMetric<int *, int*>);
mmCalc.calcMetricMatrix(desc2D, nrows, ncols, dMat);
delete [] desc2D;
return PyArray_Return(distRes);
}
else {
// unreconiged type for the matrix, throw up
throw_value_error("The array has to be of type int, float, or double for GetEuclideanDistMat");
}
} // done with an array input
else {
// REVIEW: removed a ton of code here
// we have probably have a list or a tuple
unsigned int ncols = 0;
unsigned int nrows = python::extract<unsigned int>(descripMat.attr("__len__")());
CHECK_INVARIANT(nrows > 0, "Empty list passed in");
npy_intp dMatLen = nrows*(nrows-1)/2;
distRes = (PyArrayObject *)PyArray_SimpleNew(1, &dMatLen, NPY_DOUBLE);
double *dMat = (double *)distRes->data;
// assume that we a have a list of list of values (that can be extracted to double)
std::vector<PySequenceHolder<double> > dData;
dData.reserve(nrows);
for (unsigned int i = 0; i < nrows; i++) {
//PySequenceHolder<double> row(seq[i]);
PySequenceHolder<double> row(descripMat[i]);
if(i==0) {
ncols = row.size();
} else if( row.size() != ncols ) {
throw_value_error("All subsequences must be the same length");
}
dData.push_back(row);
}
MetricMatrixCalc< std::vector<PySequenceHolder<double> >, PySequenceHolder<double> > mmCalc;
mmCalc.setMetricFunc(&EuclideanDistanceMetric< PySequenceHolder<double>, PySequenceHolder<double> >);
mmCalc.calcMetricMatrix(dData, nrows, ncols, dMat);
}
return PyArray_Return(distRes);
}
