# Author: Jintao Huang # Email: [email protected] # Date: import torch from dev.torch import corrcoef # In[0]: x = torch.randn(10, 20) print(torch.allclose(torch.corrcoef(x), corrcoef(x))) print() """Out[0] True """
def other_ops(self):
a = torch.randn(4)
b = torch.randn(4)
c = torch.randint(0, 8, (5, ), dtype=torch.int64)
e = torch.randn(4, 3)
f = torch.randn(4, 4, 4)
size = [0, 1]
dims = [0, 1]
return (
torch.atleast_1d(a),
torch.atleast_2d(a),
torch.atleast_3d(a),
torch.bincount(c),
torch.block_diag(a),
torch.broadcast_tensors(a),
torch.broadcast_to(a, (4)),
# torch.broadcast_shapes(a),
torch.bucketize(a, b),
torch.cartesian_prod(a),
torch.cdist(e, e),
torch.clone(a),
torch.combinations(a),
torch.corrcoef(a),
# torch.cov(a),
torch.cross(e, e),
torch.cummax(a, 0),
torch.cummin(a, 0),
torch.cumprod(a, 0),
torch.cumsum(a, 0),
torch.diag(a),
torch.diag_embed(a),
torch.diagflat(a),
torch.diagonal(e),
torch.diff(a),
torch.einsum("iii", f),
torch.flatten(a),
torch.flip(e, dims),
torch.fliplr(e),
torch.flipud(e),
torch.kron(a, b),
torch.rot90(e),
torch.gcd(c, c),
torch.histc(a),
torch.histogram(a),
torch.meshgrid(a),
torch.lcm(c, c),
torch.logcumsumexp(a, 0),
torch.ravel(a),
torch.renorm(e, 1, 0, 5),
torch.repeat_interleave(c),
torch.roll(a, 1, 0),
torch.searchsorted(a, b),
torch.tensordot(e, e),
torch.trace(e),
torch.tril(e),
torch.tril_indices(3, 3),
torch.triu(e),
torch.triu_indices(3, 3),
torch.vander(a),
torch.view_as_real(torch.randn(4, dtype=torch.cfloat)),
torch.view_as_complex(torch.randn(4, 2)),
torch.resolve_conj(a),
torch.resolve_neg(a),
)
def pearson_corr(inp: Tensor, target: Tensor) -> Tensor:
return torch.corrcoef(torch.stack([inp, target])).amin()