from numpy.typing import NDArray 注意,虽然numpy.typing中有与ndarray相关的类型(如NDArray),但直接导入ndarray可能并不总是可行的,因为numpy.typing可能并没有直接导出ndarray这个名字(而是导出了像NDArray这样的别名)。 如果你的代码中确实需要用到ndarray类型,并且是在类型注解的上下文中,你应该使用NDArray或其他相关...
#使用scipy模块 求定积分 from numpy import e,pi,inf,sqrt, sin, cos, tan,arctan from scipy.integrate import quad...Parameters --- func : {function, scipy.L...
from typing import Dict, List, Optional, Union, Tuple, Iterable import numpy as np from PIL import Image import torch # Imagenet mean and std IMAGENET_STANDARD_MEAN = [0.5, 0.5, 0.5] IMAGENET_STANDARD_STD = [0.5, 0.5, 0.5] def resize( image: Image.Image, size: Tuple[int, int], ...
pip install numpy You can use the `numpy` integration to return a `ndarray` object:: from neomodel import db from neomodel.integration.numpy import to_ndarray array = to_ndarray(db.cypher_query("MATCH (a:Person) RETURN a.name AS name, a.born AS born")) Logging === Expand Down 64...
import msgpack from pkg_resources import resource_filename import numpy as np from numpy.typing import ArrayLike from .._cache import cache from .._typing import _FloatLike_co with open(resource_filename(__name__, "intervals.msgpack"), "rb") as _fdesc: # We use floats for dictionary ...
fromimportlib.metadataimportversionpkgs=["matplotlib","numpy","tiktoken","torch","tensorflow"# For OpenAI's pretrained weights]forpinpkgs:print(f"{p}version: {version(p)}")# outputmatplotlibversion:3.8.0numpyversion:1.26.4tiktokenversion:0.7.0torchversion:2.3.1tensorflowversion:2.16.1 ...
importcopyimporttorchimportnumpyasnpclassVADIterator:def__init__(self,model,threshold:float=0.5,sampling_rate:int=16000,min_silence_duration_ms:int=100,speech_pad_ms:int=30,):""" Mainly taken from https://github.com/snakers4/silero-vad ...
Here, we are removing an element from the specified array.Open Compiler import array as arr # creating array numericArray = arr.array('i', [111, 211, 311, 411, 511]) # before removing array print ("Before removing:", numericArray) # removing array numericArray.remove(311) # after ...
importnumpyasnp embeddings=np.mean(np.array(out),axis=1)embeddings.shape(2,384) With two 384-dimensional vector embeddings, one for each input text. To make our lives easier, wrap the encoding process into a single function...
import numpy as np import numpy.typing as npt @@ -52,12 +50,11 @@ } if TYPE_CHECKING: Array = Any # To be changed to a Protocol later (see array-api#589) ArrayLike = Array | npt.ArrayLike Array: TypeAlias = Any # To be changed to a Protocol later (see array-api#589) Arr...