def __init__(self,X,Y,Xshape=None,Yshape=None,Xrange=None,Yrange=None,shuffle=True,max_batchsize=1):
if shuffle:
perm = random.permutation(X.shape[0])
X[:] = X[perm]
Y[:] = Y[perm]
self._X = bm.asarray(X)
self._Y = bm.asarray(Y)
self._size = X.shape[0]
self.max_batchsize = max_batchsize
self.Xshape = Xshape or (1,X.shape[1])
self.Yshape = Yshape or (1,Y.shape[1])
self.Xdim = X.shape[1]
self.Ydim = Y.shape[1]
self.Xrange = Xrange or (X.min(axis=0),X.max(axis=0))
self.Yrange = Yrange or (Y.min(axis=0),Y.max(axis=0))
rs = self._rowslice(0,self._size)
self.train = DataFold(X[rs,:],Y[rs,:])
self.valid = DataFold(X[0:0,:],Y[0:0,:])
self.test = DataFold(X[0:0,:],Y[0:0,:])
def __init__(self,
X,
Y,
Xshape=None,
Yshape=None,
Xrange=None,
Yrange=None,
shuffle=True,
max_batchsize=1):
if isinstance(X, dict):
Xtest = X['test']
Ytest = Y['test']
X = X['train']
Y = Y['train']
else:
Xtest = X[0, :] # empty
Ytest = Y[0, :] # empty
if shuffle:
perm = random.permutation(X.shape[0])
X = take(X, perm, axis=0)
if not (X is Y):
Y = take(Y, perm, axis=0)
t0 = now()
self._X = bm.asarray(X)
self._Y = bm.asarray(Y) if not (X is Y) else self._X
self._Xtest = bm.asarray(Xtest)
self._Ytest = bm.asarray(Ytest) if not (X is Y) else self._Xtest
if bm.backend_name == "gnumpy":
print "Host->Device transfer of dataset took %.3fs" % (now() - t0)
self._size = X.shape[0]
self._Xrescale = (1., 0.) #scale,bias
self.max_batchsize = max_batchsize
self.Xshape = Xshape or (1, X.shape[1])
self.Yshape = Yshape or (1, Y.shape[1])
self.Xdim = X.shape[1]
self.Ydim = Y.shape[1]
self.Xrange = Xrange or (X.min(axis=0), X.max(axis=0))
self.Yrange = Yrange or (Y.min(axis=0), Y.max(axis=0))
if not isscalar(self.Xrange[0]):
self.Xrange = (bm.asarray(self.Xrange[0]).reshape(
(1, -1)), bm.asarray(self.Xrange[1]).reshape((1, -1)))
if not isscalar(self.Yrange[0]):
self.Yrange = (bm.asarray(self.Yrange[0]).reshape(
(1, -1)), bm.asarray(self.Yrange[1]).reshape((1, -1)))
rs = self._rowslice(0, self._size)
self.train = DataFold(self._X[rs, :], self._Y[rs, :])
self.valid = DataFold(self._X[0:0, :], self._Y[0:0, :])
self.test = DataFold(self._Xtest, self._Ytest)
def __init__(self,X,Y,Xshape=None,Yshape=None,Xrange=None,Yrange=None,shuffle=True,max_batchsize=1):
if isinstance(X,dict):
Xtest = X['test']
Ytest = Y['test']
X = X['train']
Y = Y['train']
else:
Xtest = X[0,:] # empty
Ytest = Y[0,:] # empty
if shuffle:
perm = random.permutation(X.shape[0])
X = take(X,perm,axis=0)
if not (X is Y):
Y = take(Y,perm,axis=0)
t0 = now()
self._X = bm.asarray(X)
self._Y = bm.asarray(Y) if not (X is Y) else self._X
self._Xtest = bm.asarray(Xtest)
self._Ytest = bm.asarray(Ytest) if not (X is Y) else self._Xtest
if bm.backend_name == "gnumpy":
print "Host->Device transfer of dataset took %.3fs" % (now()-t0)
self._size = X.shape[0]
self._Xrescale = (1.,0.) #scale,bias
self.max_batchsize = max_batchsize
self.Xshape = Xshape or (1,X.shape[1])
self.Yshape = Yshape or (1,Y.shape[1])
self.Xdim = X.shape[1]
self.Ydim = Y.shape[1]
self.Xrange = Xrange or (X.min(axis=0),X.max(axis=0))
self.Yrange = Yrange or (Y.min(axis=0),Y.max(axis=0))
if not isscalar(self.Xrange[0]):
self.Xrange = (bm.asarray(self.Xrange[0]).reshape((1,-1)),bm.asarray(self.Xrange[1]).reshape((1,-1)))
if not isscalar(self.Yrange[0]):
self.Yrange = (bm.asarray(self.Yrange[0]).reshape((1,-1)),bm.asarray(self.Yrange[1]).reshape((1,-1)))
rs = self._rowslice(0,self._size)
self.train = DataFold(self._X[rs,:],self._Y[rs,:])
self.valid = DataFold(self._X[0:0,:],self._Y[0:0,:])
self.test = DataFold(self._Xtest,self._Ytest)
