diff options
Diffstat (limited to 'python/analytics/tranche_functions.py')
| -rw-r--r-- | python/analytics/tranche_functions.py | 31 |
1 files changed, 30 insertions, 1 deletions
diff --git a/python/analytics/tranche_functions.py b/python/analytics/tranche_functions.py index 66a1b68b..a625949f 100644 --- a/python/analytics/tranche_functions.py +++ b/python/analytics/tranche_functions.py @@ -54,6 +54,23 @@ libloss.BCloss_recov_dist.argtypes = [ ndpointer('double', ndim=2, flags='F,writeable'),# output L ndpointer('double', ndim=2, flags='F,writeable')# output R ] +libloss.BCloss_recov_trunc.restype = None +libloss.BCloss_recov_trunc.argtypes = [ + ndpointer('double', ndim=2, flags='F'),# defaultprob + POINTER(c_int),# nrow(defaultprob) + POINTER(c_int),# ncol(defaultprob) + ndpointer('double', ndim=1, flags='F'),# issuerweights + ndpointer('double', ndim=1, flags='F'),# recovery + ndpointer('double', ndim=1, flags='F'),# Z + ndpointer('double', ndim=1, flags='F'),# w + POINTER(c_int), # len(Z) = len(w) + ndpointer('double', ndim=1, flags='F'), # rho + POINTER(c_int), # Ngrid + POINTER(c_double), #K + POINTER(c_int), #defaultflag + ndpointer('double', ndim=2, flags='F,writeable'),# output L + ndpointer('double', ndim=2, flags='F,writeable')# output R +] libloss.lossdistrib_joint.restype = None libloss.lossdistrib_joint.argtypes = [ @@ -110,13 +127,25 @@ def fitprob(Z, w, rho, p0): def BCloss_recov_dist(defaultprob, issuerweights, recov, rho, Z, w, Ngrid=101, defaultflag=False): L = np.zeros((Ngrid, defaultprob.shape[1]), order='F') R = np.zeros_like(L) - rho = np.repeat(rho, issuerweights.size) + rho = np.full(issuerweights.size, rho) libloss.BCloss_recov_dist(defaultprob, byref(c_int(defaultprob.shape[0])), byref(c_int(defaultprob.shape[1])), issuerweights, recov, Z, w, byref(c_int(Z.size)), rho, byref(c_int(Ngrid)), byref(c_int(defaultflag)), L, R) return L, R +def BCloss_recov_trunc(defaultprob, issuerweights, recov, rho, K, Z, w, Ngrid=101, defaultflag=False): + L = np.zeros((Ngrid, defaultprob.shape[1]), order='F') + R = np.zeros_like(L) + rho = np.full(issuerweights.size, rho) + libloss.BCloss_recov_trunc(defaultprob, byref(c_int(defaultprob.shape[0])), + byref(c_int(defaultprob.shape[1])), + issuerweights, recov, Z, w, byref(c_int(Z.size)), + rho, byref(c_int(Ngrid)), byref(c_double(K)), + byref(c_int(defaultflag)), + L, R) + return L, R + def lossdistrib_joint(p, pp, w, S, Ngrid=101, defaultflag=False): """Joint loss-recovery distribution recursive algorithm. |