def _stop_training(self):
try:
inv_xTx = utils.inv(self._xTx + self.ridge_param *
mdp.numx.eye(self._input_dim + 1))
except numx_linalg.LinAlgError, exception:
errstr = (str(exception) +
"\n Input data may be redundant (i.e., some of the " +
"variables may be linearly dependent).")
raise NodeException(errstr)
def _stop_training(self):
self.labels = self._cov_objs.keys()
self.labels.sort()
nitems = 0
for lbl in self.labels:
cov, mean, p = self._cov_objs[lbl].fix()
nitems += p
self._sqrt_def_covs.append(numx.sqrt(numx_linalg.det(cov)))
if self._sqrt_def_covs[-1] == 0.0:
err = "The covariance matrix is singular for at least " "one class."
raise mdp.NodeException(err)
self.means.append(mean)
self.p.append(p)
self.inv_covs.append(utils.inv(cov))
for i in range(len(self.p)):
self.p[i] /= float(nitems)
del self._cov_objs
def _stop_training(self):
self.labels = self._cov_objs.keys()
self.labels.sort()
nitems = 0
for lbl in self.labels:
cov, mean, p = self._cov_objs[lbl].fix()
nitems += p
self._sqrt_def_covs.append(numx.sqrt(numx_linalg.det(cov)))
if self._sqrt_def_covs[-1] == 0.0:
err = ("The covariance matrix is singular for at least "
"one class.")
raise mdp.NodeException(err)
self.means.append(mean)
self.p.append(p)
self.inv_covs.append(utils.inv(cov))
for i in range(len(self.p)):
self.p[i] /= float(nitems)
del self._cov_objs
def main(argv):
inputfile = None
number = None
try:
opts, args = getopt.getopt(argv,"hi:n:")
except getopt.GetoptError:
print 'learn.py -i <inputfile>'
print 'eg. for test the reservoir: python learn.py -i /var/www/isolated/octave/two'
print 'eg. to make the reservoir learn: python learn.py -i /var/www/isolated/octave/two -n 0'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'learn.py -i <inputfile>'
print 'eg. for test the reservoir: python learn.py -i /var/www/isolated/octave/two'
print 'eg. to make the reservoir learn: python learn.py -i /var/www/isolated/octave/two -n 0'
sys.exit()
elif opt in ("-i"):
inputfile = arg
elif opt in ("-n"):
number = arg
#print number
#sys.exit()
#print 'Input file is: ', inputfile
dirname= inputfile.rsplit(os.sep,1)[0]
#print 'dirname is: ', dirname
#sys.exit()
if inputfile is None:
print 'no input file'
sys.exit()
test_sample_file = inputfile
if number is None:
cwd = os.getcwd()
os.chdir('octave')
#cmd = "octave -q wav2mat.m " + test_dir + os.sep + test_sample_file + " 8000 128 > null"
cmd = "octave -q wav2mat.m " + test_sample_file + " 8000 128 > null"
#print 'cmd is: ', cmd
os.system(cmd)
os.chdir(cwd)
content = loadmat(test_sample_file + ".mat")
test_input = content['spec'].T
input_dim = test_input.shape[1]
#readout = Oger.nodes.RidgeRegressionNode(use_pinv=True, input_dim=100)
try:
pinput = open(dirname + os.sep + 'reservoir.pkl', 'rb')
reservoir = pickle.load(pinput)
pinput.close()
except IOError:
reservoir = Oger.nodes.LeakyReservoirNode(input_dim=input_dim, output_dim=100, input_scaling=.1, leak_rate=.3)
poutput = open(dirname + os.sep + 'reservoir.pkl', 'wb')
pickle.dump(reservoir, poutput)
poutput.close()
if number is None:
#gn = guess(test_input, reservoir, readout, dirname);
gn = guess(test_input, reservoir, dirname);
print gn
sys.exit()
#shutil.copy(test_sample_file + ".mat", test_sample_file + "_" + numba + ".mat")
x = reservoir.execute(test_input)
teacher_inputs = [test_input]
teacher_outputs = [-1 * mdp.numx.ones([teacher_inputs[-1].shape[0], 10])]
teacher_outputs[-1][:, number] = 1
"""
readout._check_train_args(x, *teacher_outputs)
readout.train(x, *teacher_outputs)
readout._stop_training()
"""
#print 'dirname: ', dirname + os.sep + 'xtx.mat'
#if readout._xTx is None:
try:
xTx = np.loadtxt(dirname + os.sep + 'xtx.mat')
xTy = np.loadtxt(dirname + os.sep + 'xty.mat')
except IOError:
input_dim = 100
#readout._set_output_dim(10)
output_dim = 10
#readout._dtype
dtype = "float64"
x_size = input_dim
#readout._xTx = numx.zeros((x_size, x_size), dtype)
xTx = numx.zeros((x_size, x_size), dtype)
#readout._xTy = numx.zeros((x_size, output_dim), dtype)
xTy = numx.zeros((x_size, output_dim), dtype)
# update internal variables
#readout._xTx += mult(x.T, x)
xTx += mult(x.T, x)
#readout._xTy += mult(x.T, *teacher_outputs)
xTy += mult(x.T, *teacher_outputs)
# calculate beta
#inv_xTx = utils.inv(readout._xTx)
inv_xTx = utils.inv(xTx)
#readout.beta = mult(inv_xTx, readout._xTy)
beta = mult(inv_xTx, xTy)
# save everything to file
np.savetxt(dirname + os.sep + 'xtx.mat' , xTx)
np.savetxt(dirname + os.sep + 'xty.mat' , xTy)
#np.savetxt(dirname + os.sep + 'invxtx.mat', inv_xTx)
np.savetxt(dirname + os.sep + 'beta.mat' , beta)
print 99
