>>>importpandasaspd>>>pd.options.display.max_columns =20>>>importnumpyasnp>>>rng = np.random.RandomState(seed=5)>>>ints = rng.randint(1,11, size=(3,2))>>>a = ["A","B","C"]>>>b = ["D","A","E"]>>>df = pd.DataFrame
import numpy as np from scipy._lib._array_api import array_namespace, is_numpy, xp_size @@ -69,13 +66,14 @@ except TypeError: copy_if_needed = False _RNG: TypeAlias = np.random.Generator | np.random.RandomState SeedType: TypeAlias = IntNumber | _RNG | None GeneratorType = TypeV...
import fdasrsf.bayesian_functions as bf 11 import fdasrsf.fPCA as fpca ~/anaconda3/lib/python3.7/site-packages/fdasrsf/utility_functions.py in <module> 19 from joblib import Parallel, delayed 20 import numpy.random as rn ---> 21 import optimum_reparamN2 as orN2 22 import optimum_...
random_state:int、RandomState 实例或无,默认=无 当子样本不是None并且kind是'both'或'individual'时,控制所选样本的随机性。有关详细信息,请参阅词汇表。 返回: display:PartialDependenceDisplay 例子: >>>importmatplotlib.pyplotasplt>>>fromsklearn.datasetsimportmake_friedman1>>>fromsklearn.ensembleimportGr...
import numpy as np from sklearn.utils import resample # 示例数据 X = np.array([[1., 0.], [2., 1.], [0., 0.]]) y = np.array([0, 1, 2]) # 使用 resample 进行重采样 X_resampled, y_resampled = resample(X, y, n_samples=2, random_state=0) print("X_resampled:") print...
>>> import pandas as pd >>> pd.options.display.max_columns = 20 >>> import numpy as np >>> rng = np.random.RandomState(seed=5) >>> ints = rng.randint(1, 11, size=(3, 2)) >>> a = ["A", "B", "C"] >>> b = ["D", "A", "E"] >>> df = pd.DataFrame(int...
n_estimators=10, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False) I tried using number of trees =1,5,10 as per your example but not working could you pls say me where shld i need to make changes and moreover when i set randomstate = none each time i...
edge_key:str 或无,可选(默认=无)。边缘键的有效列名(对于 MultiGraph)。如果 create_using 是多重图,则此列中的值用于添加边时的边键。 例子 边上的简单整数权重: >>>importpandasaspd>>>pd.options.display.max_columns=20>>>importnumpyasnp>>>rng=np.random.RandomState(seed=5)>>>ints=rng.randint...
import numpy as npdef have_fenv() -> bool: ...def random_double(size: int, rng: np.random.RandomState) -> np.float64: ... def test_add_round(size: int, mode: str, rng: np.random.RandomState): ...def random_double(size: int) -> np.float64: ... def test_add_round(size:...
import logging import random from functools import partial from typing import Any, Sequence, Union import numpy as np import torch from torch.utils.data import DataLoader from typeguard import check_argument_types from espnet2.iterators.abs_iter_factory import AbsIterFactory from espnet2.samplers.abs...