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from abc import ABC, abstractmethod
import numpy as np
class BackendInterface(ABC):
# init
@abstractmethod
def array(self, x):
raise NotImplementedError()
@abstractmethod
def copy(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def zeros(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def ones(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def zeros_like(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def ones_like(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def arange(self, *args, **kwargs):
raise NotImplementedError()
# double tensor
@abstractmethod
def add(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def subtract(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def multiply(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def divide(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def matmul(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def dot(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def power(self, x, y, *args, **kwargs):
raise NotImplementedError()
# single tensor
@abstractmethod
def square(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def sqrt(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def log(self, x, *args, **kwargs):
raise NotImplementedError()
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@abstractmethod
def exp(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def sin(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def cos(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def tan(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def sinh(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def cosh(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def tanh(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def abs(self, x, *args, **kwargs):
raise NotImplementedError()
# signs
@abstractmethod
def sign(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def positive(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def negative(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def negative(self, x, *args, **kwargs):
raise NotImplementedError()
# Compare
@abstractmethod
def equal(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def not_equal(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def less(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def less_equal(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def greater(self, x, y, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def greater_equal(self, x, y, *args, **kwargs):
raise NotImplementedError()
# logic
@abstractmethod
def logical_and(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def logical_or(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def logical_xor(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def logical_not(self, x, *args, **kwargs):
raise NotImplementedError()
# shaping
@abstractmethod
def flatten(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def reshape(self, x, *args, **kwargs):
raise NotImplementedError()
# broadcasting
@abstractmethod
def broadcast_to(self, x, *args, **kwargs):
raise NotImplementedError()
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@abstractmethod
def repeat(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def tile(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def concatenate(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def split(self, x, *args, **kwargs):
raise NotImplementedError()
# reduce
@abstractmethod
def sum(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def prod(self, x, *args, **kwargs):
raise NotImplementedError()
# min/max etc
@abstractmethod
def max(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def min(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def mean(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def var(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def std(self, x, *args, **kwargs):
raise NotImplementedError()
# others
@abstractmethod
def pad(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def insert(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def transpose(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def where(self, x, *args, **kwargs):
raise NotImplementedError()
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@abstractmethod
def cumsum(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def cumprod(self, x, *args, **kwargs):
raise NotImplementedError()
# not working yet
@abstractmethod
def as_strided(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def sliding_window_view(self, x, *args, **kwargs):
raise NotImplementedError()
@abstractmethod
def einsum(self, subscript, x, y, *args, **kwargs):
raise NotImplementedError()
class NumpyBackend(BackendInterface):
import numpy as np
# init
def array(self, x):
return self.np.array(x)
def copy(self, x, *args, **kwargs):
return self.np.copy(x, *args, **kwargs)
def zeros(self, x, *args, **kwargs):
return self.np.zeros(x, *args, **kwargs)
def ones(self, x, *args, **kwargs):
return self.np.ones(x, *args, **kwargs)
def zeros_like(self, x, *args, **kwargs):
return self.np.zeros_like(x, *args, **kwargs)
def ones_like(self, x, *args, **kwargs):
return self.np.ones_like(x, *args, **kwargs)
def arange(self, *args, **kwargs):
return self.np.arange(*args, **kwargs)
# double tensor
def add(self, x, y, *args, **kwargs):
return self.np.add(x, y, *args, **kwargs)
def subtract(self, x, y, *args, **kwargs):
return self.np.subtract(x, y, *args, **kwargs)
def multiply(self, x, y, *args, **kwargs):
return self.np.multiply(x, y, *args, **kwargs)
def divide(self, x, y, *args, **kwargs):
return self.np.divide(x, y, *args, **kwargs)
def matmul(self, x, y, *args, **kwargs):
return self.np.matmul(x, y, *args, **kwargs)
def dot(self, x, y, *args, **kwargs):
return self.np.dot(x, y, *args, **kwargs)
def power(self, x, y, *args, **kwargs):
return self.np.power(x, y, *args, **kwargs)
# single tensor
def square(self, x, *args, **kwargs):
return self.np.square(x, *args, **kwargs)
def sqrt(self, x, *args, **kwargs):
return self.np.sqrt(x, *args, **kwargs)
def log(self, x, *args, **kwargs):
return self.np.log(x, *args, **kwargs)
def exp(self, x, *args, **kwargs):
return self.np.exp(x, *args, **kwargs)
def sin(self, x, *args, **kwargs):
return self.np.sin(x, *args, **kwargs)
def cos(self, x, *args, **kwargs):
return self.np.cos(x, *args, **kwargs)
def tan(self, x, *args, **kwargs):
return self.np.tan(x, *args, **kwargs)
def sinh(self, x, *args, **kwargs):
return self.np.sinh(x, *args, **kwargs)
def cosh(self, x, *args, **kwargs):
return self.np.cosh(x, *args, **kwargs)
def tanh(self, x, *args, **kwargs):
return self.np.tanh(x, *args, **kwargs)
def abs(self, x, *args, **kwargs):
return self.np.abs(x, *args, **kwargs)
# signs
def sign(self, x, *args, **kwargs):
return self.np.sign(x, *args, **kwargs)
def positive(self, x, *args, **kwargs):
return self.np.positive(x, *args, **kwargs)
def negative(self, x, *args, **kwargs):
return self.np.negative(x, *args, **kwargs)
# compare
def equal(self, x, y, *args, **kwargs):
return self.np.equal(x, y, *args, **kwargs)
def not_equal(self, x, y, *args, **kwargs):
return self.np.not_equal(x, y, *args, **kwargs)
def less(self, x, y, *args, **kwargs):
return self.np.less(x, y, *args, **kwargs)
def less_equal(self, x, y, *args, **kwargs):
return self.np.less_equal(x, y, *args, **kwargs)
def greater(self, x, y, *args, **kwargs):
return self.np.greater(x, y, *args, **kwargs)
def greater_equal(self, x, y, *args, **kwargs):
return self.np.greater_equal(x, y, *args, **kwargs)
# logic
def logical_and(self, x, *args, **kwargs):
return self.np.logical_and(x, *args, **kwargs)
def logical_or(self, x, *args, **kwargs):
return self.np.logical_or(x, *args, **kwargs)
def logical_xor(self, x, *args, **kwargs):
return self.np.logical_xor(x, *args, **kwargs)
def logical_not(self, x, *args, **kwargs):
return self.np.logical_not(x, *args, **kwargs)
# shaping
def flatten(self, x, **kwargs):
return self.np.reshape(x, -1, **kwargs)
def reshape(self, x, *args, **kwargs):
return self.np.reshape(x, *args, **kwargs)
# broadcasting
def broadcast_to(self, x, *args, **kwargs):
return self.np.broadcast_to(x, *args, **kwargs)
def repeat(self, x, *args, **kwargs):
return self.np.repeat(x, *args, **kwargs)
def tile(self, x, *args, **kwargs):
return self.np.tile(x, *args, **kwargs)
def concatenate(self, x, *args, **kwargs):
return self.np.concatenate(x, *args, **kwargs)
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def split(self, x, *args, **kwargs):
return self.np.split(x, *args, **kwargs)
# reduce
def sum(self, x, *args, **kwargs):
return self.np.sum(x, *args, **kwargs)
def prod(self, x, *args, **kwargs):
return self.np.prod(x, *args, **kwargs)
# min/max etc
def max(self, x, *args, **kwargs):
return self.np.max(x, *args, **kwargs)
def min(self, x, *args, **kwargs):
return self.np.min(x, *args, **kwargs)
def mean(self, x, *args, **kwargs):
return self.np.mean(x, *args, **kwargs)
def var(self, x, *args, **kwargs):
return self.np.var(x, *args, **kwargs)
def std(self, x, *args, **kwargs):
return self.np.std(x, *args, **kwargs)
# others
def pad(self, x, *args, **kwargs):
return self.np.pad(x, *args, **kwargs)
def insert(self, x, *args, **kwargs):
return self.np.insert(x, *args, **kwargs)
def transpose(self, x, *args, **kwargs):
return self.np.transpose(x, *args, **kwargs)
def where(self, x, *args, **kwargs):
return self.np.where(x, *args, **kwargs)
def cumsum(self, x, *args, **kwargs):
return self.np.cumsum(x, *args, **kwargs)
def cumprod(self, x, *args, **kwargs):
return self.np.cumprod(x, *args, **kwargs)
# not working yet
def as_strided(self, x, *args, **kwargs):
return self.np.lib.stride_tricks.as_strided(x, *args, **kwargs)
def sliding_window_view(self, x, *args, **kwargs):
return self.np.lib.stride_tricks.sliding_window_view(x, *args, **kwargs)
def einsum(self, subscript, x, y, *args, **kwargs):
return self.np.einsum(subscript, x, y, *args, **kwargs)
class CupyBackend(BackendInterface):
try:
import cupy as cp
except ImportError:
pass
# init
def array(self, x):
return self.cp.array(x)
def copy(self, x, *args, **kwargs):
return self.cp.copy(x, *args, **kwargs)
def zeros(self, x, *args, **kwargs):
return self.cp.zeros(x, *args, **kwargs)
def ones(self, x, *args, **kwargs):
return self.cp.ones(x, *args, **kwargs)
def zeros_like(self, x, *args, **kwargs):
return self.cp.zeros_like(x, *args, **kwargs)
def ones_like(self, x, *args, **kwargs):
return self.cp.ones_like(x, *args, **kwargs)
def arange(self, *args, **kwargs):
return self.cp.arange(*args, **kwargs)
# double tensor
def add(self, x, y, *args, **kwargs):
return self.cp.add(x, y, *args, **kwargs)
def subtract(self, x, y, *args, **kwargs):
return self.cp.subtract(x, y, *args, **kwargs)
def multiply(self, x, y, *args, **kwargs):
return self.cp.multiply(x, y, *args, **kwargs)
def divide(self, x, y, *args, **kwargs):
return self.cp.divide(x, y, *args, **kwargs)
def matmul(self, x, y, *args, **kwargs):
return self.cp.matmul(x, y, *args, **kwargs)
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def dot(self, x, y, *args, **kwargs):
return self.cp.dot(x, y, *args, **kwargs)
def power(self, x, y, *args, **kwargs):
return self.cp.power(x, y, *args, **kwargs)
# single tensor
def square(self, x, *args, **kwargs):
return self.cp.square(x, *args, **kwargs)
def sqrt(self, x, *args, **kwargs):
return self.cp.sqrt(x, *args, **kwargs)
def log(self, x, *args, **kwargs):
return self.cp.log(x, *args, **kwargs)
def exp(self, x, *args, **kwargs):
return self.cp.exp(x, *args, **kwargs)
def sin(self, x, *args, **kwargs):
return self.cp.sin(x, *args, **kwargs)
def cos(self, x, *args, **kwargs):
return self.cp.cos(x, *args, **kwargs)
def tan(self, x, *args, **kwargs):
return self.cp.tan(x, *args, **kwargs)
def sinh(self, x, *args, **kwargs):
return self.cp.sinh(x, *args, **kwargs)
def cosh(self, x, *args, **kwargs):
return self.cp.cosh(x, *args, **kwargs)
def tanh(self, x, *args, **kwargs):
return self.cp.tanh(x, *args, **kwargs)
def abs(self, x, *args, **kwargs):
return self.cp.abs(x, *args, **kwargs)
# signs
def sign(self, x, *args, **kwargs):
return self.cp.sign(x, *args, **kwargs)
def positive(self, x, *args, **kwargs):
return self.cp.positive(x, *args, **kwargs)
def negative(self, x, *args, **kwargs):
return self.cp.negative(x, *args, **kwargs)
# compare
def equal(self, x, y, *args, **kwargs):
return self.cp.equal(x, y, *args, **kwargs)
def not_equal(self, x, y, *args, **kwargs):
return self.cp.not_equal(x, y, *args, **kwargs)
def less(self, x, y, *args, **kwargs):
return self.cp.less(x, y, *args, **kwargs)
def less_equal(self, x, y, *args, **kwargs):
return self.cp.less_equal(x, y, *args, **kwargs)
def greater(self, x, y, *args, **kwargs):
return self.cp.greater(x, y, *args, **kwargs)
def greater_equal(self, x, y, *args, **kwargs):
return self.cp.greater_equal(x, y, *args, **kwargs)
# logic
def logical_and(self, x, *args, **kwargs):
return self.cp.logical_and(x, *args, **kwargs)
def logical_or(self, x, *args, **kwargs):
return self.cp.logical_or(x, *args, **kwargs)
def logical_xor(self, x, *args, **kwargs):
return self.cp.logical_xor(x, *args, **kwargs)
def logical_not(self, x, *args, **kwargs):
return self.cp.logical_not(x, *args, **kwargs)
# shaping
def flatten(self, x, **kwargs):
return self.cp.reshape(x, -1, **kwargs)
def reshape(self, x, *args, **kwargs):
return self.cp.reshape(x, *args, **kwargs)
# broadcasting
def broadcast_to(self, x, *args, **kwargs):
return self.cp.broadcast_to(x, *args, **kwargs)
def repeat(self, x, *args, **kwargs):
return self.cp.repeat(x, *args, **kwargs)
def tile(self, x, *args, **kwargs):
return self.cp.tile(x, *args, **kwargs)
def concatenate(self, x, *args, **kwargs):
return self.cp.concatenate(x, *args, **kwargs)
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def split(self, x, *args, **kwargs):
return self.cp.split(x, *args, **kwargs)
# reduce
def sum(self, x, *args, **kwargs):
return self.cp.sum(x, *args, **kwargs)
def prod(self, x, *args, **kwargs):
return self.cp.prod(x, *args, **kwargs)
# min/max etc
def max(self, x, *args, **kwargs):
return self.cp.max(x, *args, **kwargs)
def min(self, x, *args, **kwargs):
return self.cp.min(x, *args, **kwargs)
def mean(self, x, *args, **kwargs):
return self.cp.mean(x, *args, **kwargs)
def var(self, x, *args, **kwargs):
return self.cp.var(x, *args, **kwargs)
def std(self, x, *args, **kwargs):
return self.cp.std(x, *args, **kwargs)
# others
def pad(self, x, *args, **kwargs):
return self.cp.pad(x, *args, **kwargs)
def insert(self, x, *args, **kwargs):
return self.cp.insert(x, *args, **kwargs)
def transpose(self, x, *args, **kwargs):
return self.cp.negative(x, *args, **kwargs)
def where(self, x, *args, **kwargs):
return self.cp.where(x, *args, **kwargs)
def cumsum(self, x, *args, **kwargs):
return self.cp.cumsum(x, *args, **kwargs)
def cumprod(self, x, *args, **kwargs):
return self.cp.cumprod(x, *args, **kwargs)
# not working yet
def as_strided(self, x, *args, **kwargs):
return self.cp.lib.stride_tricks.as_strided(x, *args, **kwargs)
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def sliding_window_view(self, x, *args, **kwargs):
return self.cp.lib.stride_tricks.sliding_window_view(x, *args, **kwargs)
def einsum(self, subscript, x, y, *args, **kwargs):
return self.cp.einsum(subscript, x, y, *args, **kwargs)
class NumbaBackend(BackendInterface):
from numba import jit
# init
@staticmethod
@jit(nopython=True)
def array(x):
return np.array(x)
@staticmethod
@jit(nopython=True)
def copy(x, *args, **kwargs):
return np.copy(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def zeros(x, *args, **kwargs):
return np.zeros(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def ones(x, *args, **kwargs):
return np.ones(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def zeros_like(x, *args, **kwargs):
return np.zeros_like(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def ones_like(x, *args, **kwargs):
return np.ones_like(x, *args, **kwargs)
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@staticmethod
@jit(nopython=True)
def arange(*args, **kwargs):
return np.arange(*args, **kwargs)
# double tensor
@staticmethod
@jit(nopython=True)
def add(x, y, *args, **kwargs):
return np.add(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def subtract(x, y, *args, **kwargs):
return np.subtract(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def multiply(x, y, *args, **kwargs):
return np.multiply(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def divide(x, y, *args, **kwargs):
return np.divide(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def matmul(x, y, *args, **kwargs):
return np.matmul(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def dot(x, y, *args, **kwargs):
return np.dot(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def power(x, y, *args, **kwargs):
return np.power(x, y, *args, **kwargs)
# single tensor
@staticmethod
@jit(nopython=True)
def square(x, *args, **kwargs):
return np.square(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def sqrt(x, *args, **kwargs):
return np.sqrt(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def log(x, *args, **kwargs):
return np.log(x, *args, **kwargs)
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@staticmethod
@jit(nopython=True)
def exp(x, *args, **kwargs):
return np.exp(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def sin(x, *args, **kwargs):
return np.sin(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def cos(x, *args, **kwargs):
return np.cos(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def tan(x, *args, **kwargs):
return np.tan(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def sinh(x, *args, **kwargs):
return np.sinh(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def cosh(x, *args, **kwargs):
return np.cosh(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def tanh(x, *args, **kwargs):
return np.tanh(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def abs(x, *args, **kwargs):
return np.abs(x, *args, **kwargs)
# signs
@staticmethod
@jit(nopython=True)
def sign(x, *args, **kwargs):
return np.sign(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def positive(x, *args, **kwargs):
return np.positive(x, *args, **kwargs)
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@staticmethod
@jit(nopython=True)
def negative(x, *args, **kwargs):
return np.negative(x, *args, **kwargs)
# compare
@staticmethod
@jit(nopython=True)
def equal(x, y, *args, **kwargs):
return np.equal(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def not_equal(x, y, *args, **kwargs):
return np.not_equal(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def less(x, y, *args, **kwargs):
return np.less(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def less_equal(x, y, *args, **kwargs):
return np.less_equal(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def greater(x, y, *args, **kwargs):
return np.greater(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def greater_equal(x, y, *args, **kwargs):
return np.greater_equal(x, y, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def logical_and(x, *args, **kwargs):
return np.logical_and(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def logical_or(x, *args, **kwargs):
return np.logical_or(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def logical_xor(x, *args, **kwargs):
return np.logical_xor(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def logical_not(x, *args, **kwargs):
return np.logical_not(x, *args, **kwargs)
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# shaping
@staticmethod
@jit(nopython=True)
def flatten(x, **kwargs):
return np.reshape(x, -1, **kwargs)
@staticmethod
@jit(nopython=True)
def reshape(x, *args, **kwargs):
return np.reshape(x, *args, **kwargs)
# broadcasting
@staticmethod
@jit(nopython=True)
def broadcast_to(x, *args, **kwargs):
return np.broadcast_to(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def repeat(x, *args, **kwargs):
return np.repeat(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def tile(x, *args, **kwargs):
return np.tile(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def concatenate(x, *args, **kwargs):
return np.concatenate(x, *args, **kwargs)
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@staticmethod
@jit(nopython=True)
def split(x, *args, **kwargs):
return np.split(x, *args, **kwargs)
# reduce
@staticmethod
@jit(nopython=True)
def sum(x, *args, **kwargs):
return np.sum(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def prod(x, *args, **kwargs):
return np.prod(x, *args, **kwargs)
# min/max etc
@staticmethod
@jit(nopython=True)
def max(x, *args, **kwargs):
return np.max(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def min(x, *args, **kwargs):
return np.min(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def mean(x, *args, **kwargs):
return np.mean(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def var(x, *args, **kwargs):
return np.var(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def std(x, *args, **kwargs):
return np.std(x, *args, **kwargs)
# others
@staticmethod
@jit(nopython=True)
def pad(x, *args, **kwargs):
return np.pad(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def insert(x, *args, **kwargs):
return np.insert(x, *args, **kwargs)
@staticmethod
@jit(nopython=True)
def transpose(x, *args, **kwargs):
return np.transpose(x, *args, **kwargs)