Revision c37a55579df7095a5ee66a62a5b99281bdf01418 authored by TUNA Caglayan on 01 September 2021, 09:32:34 UTC, committed by TUNA Caglayan on 01 September 2021, 09:32:34 UTC
1 parent 4772692
numpy_backend.py
import numpy as np
from .core import Backend
class NumpyBackend(Backend):
backend_name = 'numpy'
@staticmethod
def context(tensor):
return {'dtype': tensor.dtype}
@staticmethod
def tensor(data, dtype=None):
return np.array(data, dtype=dtype)
@staticmethod
def is_tensor(tensor):
return isinstance(tensor, np.ndarray)
@staticmethod
def to_numpy(tensor):
return np.copy(tensor)
@staticmethod
def shape(tensor):
return tensor.shape
@staticmethod
def ndim(tensor):
return tensor.ndim
@staticmethod
def clip(tensor, a_min=None, a_max=None):
return np.clip(tensor, a_min, a_max)
@staticmethod
def dot(a, b):
return a.dot(b)
@staticmethod
def lstsq(a, b):
x, residuals, _, _ = np.linalg.lstsq(a, b, rcond=None)
return x, residuals
def kr(self, matrices, weights=None, mask=None):
n_columns = matrices[0].shape[1]
n_factors = len(matrices)
start = ord('a')
common_dim = 'z'
target = ''.join(chr(start + i) for i in range(n_factors))
source = ','.join(i + common_dim for i in target)
operation = source + '->' + target + common_dim
if weights is not None:
matrices = [m if i else m*self.reshape(weights, (1, -1)) for i, m in enumerate(matrices)]
m = mask.reshape((-1, 1)) if mask is not None else 1
return np.einsum(operation, *matrices).reshape((-1, n_columns))*m
@staticmethod
def sort(tensor, axis, descending = False):
if descending:
return np.flip(np.sort(tensor, axis=axis), axis = axis)
else:
return np.sort(tensor, axis=axis)
for name in ['int64', 'int32', 'float64', 'float32', 'complex128', 'complex64',
'reshape', 'moveaxis', 'any', 'trace',
'where', 'copy', 'transpose', 'arange', 'ones', 'zeros', 'flip',
'zeros_like', 'eye', 'kron', 'concatenate', 'max', 'min', 'matmul',
'all', 'mean', 'sum', 'cumsum', 'prod', 'sign', 'abs', 'sqrt', 'argmin',
'argmax', 'stack', 'conj', 'diag', 'einsum', 'log2', 'tensordot', 'sin', 'cos']:
NumpyBackend.register_method(name, getattr(np, name))
for name in ['solve', 'qr', 'svd', 'eigh']:
NumpyBackend.register_method(name, getattr(np.linalg, name))
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