type=int,
default=None,
help="The device to use, e.g. CUDA device.")
parser.add_argument(
"-m",
"--method",
type=str,
default="L-BFGS-B",
help=
"The optimization algorithm to use. Valid values are COBYLA and L-BFGS-B.")
args = parser.parse_args()
if args.device is not None:
smat.set_backend_options(device=args.device)
print "Using device", smat.get_backend_info().device
print "Using method", args.method, "with float64"
# Load some sample bio data. Specifically this is a subset of the
# RNAcompete protein binding affinities from Ray et al., Nature, 2013.
y = numpy.load('data/rnac/rnac_subset.npz')['y']
n, m = y.shape
def objective_function(x, y, lib):
# The test objective function below happens to be that corresponding to
# "Variance Stabilization" (Huber et al., Bioinformatics, 2002).
# The specific objective is not important.
# The point is that the parameters can be sent to the GPU,
# evaluated, pulled back, and STILL be much faster than CPU.
import numpy
import numpy.random
import smat
import smat.util
import argparse
import scipy.optimize
parser = argparse.ArgumentParser(description="Train a 784-1000-1000-10 neural net on MNIST and print out the error rates.")
parser.add_argument("-d","--device",type=int,default=None,help="The device to use, e.g. CUDA device.")
parser.add_argument("-m","--method",type=str,default="L-BFGS-B",help="The optimization algorithm to use. Valid values are COBYLA and L-BFGS-B.")
args = parser.parse_args()
if args.device is not None:
smat.set_backend_options(device=args.device)
print "Using device",smat.get_backend_info().device
print "Using method",args.method,"with float64"
# Load some sample bio data. Specifically this is a subset of the
# RNAcompete protein binding affinities from Ray et al., Nature, 2013.
y = numpy.load('data/rnac/rnac_subset.npz')['y']
n,m = y.shape
def objective_function(x,y,lib):
# The test objective function below happens to be that corresponding to
# "Variance Stabilization" (Huber et al., Bioinformatics, 2002).
# The specific objective is not important.
# The point is that the parameters can be sent to the GPU,
# evaluated, pulled back, and STILL be much faster than CPU.
# Shorthand for some functions that we're getting from lib=smat/numpy
continue
A = smat.ones((n,k),dtype=dt)
B = smat.ones((k,m),dtype=dt)
smat.dot(A,B)
#smat.dot_nt(A,B.T)
#smat.dot_tn(A.T,B)
#smat.dot_tt(A.T,B.T)
print n,m,k
quit()
'''
parser = argparse.ArgumentParser(description="Run the smat unit tests and/or performance tests.")
parser.add_argument("-p","--perf",action="store_true",default=False,help="Run performance tests instead of unit tests.")
parser.add_argument("-d","--device",type=int,default=None,help="The device to use, e.g. CUDA device.")
parser.add_argument("-b","--backend",type=str,default=None,help="The backend to use. Currently only \"cuda\" is supported.")
args = parser.parse_args()
if args.backend is not None:
smat.set_backend(args.backend)
if args.device is not None:
smat.set_backend_options(device=args.device)
print smat.get_backend_info()
print smat.get_heap_status()
if args.perf:
smat.tests.perftest()
else:
smat.tests.unittest()
# supplementary software release for DeepBind. Users of DeepBind
# are encouraged to instead use the latest source code and binaries
# for scoring sequences at
# http://tools.genes.toronto.edu/deepbind/
#
import argparse
import smat
import smat.tests
smat.set_backend_options(device=0)
parser = argparse.ArgumentParser(description="Run the smat unit tests and/or performance tests.")
parser.add_argument("-p","--perf",action="store_true",default=False,help="Run performance tests instead of unit tests.")
parser.add_argument("-d","--device",type=int,default=None,help="The device to use, e.g. CUDA device.")
parser.add_argument("-b","--backend",type=str,default=None,help="The backend to use. Currently only \"cuda\" is supported.")
args = parser.parse_args()
if args.backend is not None:
smat.set_backend(args.backend)
if args.device is not None:
smat.set_backend_options(device=args.device)
print smat.get_backend_info()
print smat.get_heap_status()
if args.perf:
smat.tests.perftest()
else:
smat.tests.unittest()