\!h> ,SSKrSSKJr SSjrSSjrg)N)check_XycU(a[RRU5 [US5nUHn[U5U:XaMe [RR U5nUVs/sHo3UPM sn$s snf)aShuffle NumPy arrays in unison. Parameters ---------- arrays : array-like, shape = [n_arrays] A list of NumPy arrays. random_seed : int (default: None) Sets the random state. Returns ---------- shuffled_arrays : A list of NumPy arrays after shuffling. Examples -------- >>> import numpy as np >>> from mlxtend.preprocessing import shuffle_arrays_unison >>> X1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> y1 = np.array([1, 2, 3]) >>> X2, y2 = shuffle_arrays_unison(arrays=[X1, y1], random_seed=3) >>> assert(X2.all() == np.array([[4, 5, 6], [1, 2, 3], [7, 8, 9]]).all()) >>> assert(y2.all() == np.array([2, 1, 3]).all()) >>> For more usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/shuffle_arrays_unison/ r)nprandomseedlen permutation)arrays random_seednaidxs pC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\mlxtend/preprocessing/shuffle.pyshuffle_arrays_unisonr sl: {# F1IA 1v{{ ))   "C" #FqcFF ## #s2BcB[XSS9 US::dUS:a [S5eU(a+[UR5UR5/US9upVOUR5UR5pe[ X1R S-5nUSUUSUpXWSXgSpXX4$) aSplits feature and target arrays into training and test subsets. Parameters ---------- X : array-like, shape = [n_samples, n_features] Initial dataset, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. shuffle : bool (default: True) Doesn't shuffle the arrays if False train_size : float (default: 0.75) Proportion of data in the training arrays. For example, 0.75 will put 75% of the data into the training array, and 25% of the data into the test array. random_seed : int (default: None) Sets the random state. Returns ---------- X_train : array-like, shape = [n_samples * train_size, n_features] Training dataset, where n_samples is the number of samples and n_features is the number of features. y_train : array-like, shape = [n_samples * train_size] Training target values. X_test : array-like, shape = [n_samples * (1-train_size), n_features] Dataset for testing, where n_samples is the number of samples and n_features is the number of features. y_test : array-like, shape = [n_samples * (1-train_size)] Target values for testing. For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/shuffled_split/ F)y_intgg?z2train_size must be a float in the range (0.0, 1.0))r r rN)r ValueErrorrcopyroundshape) Xyshuffle train_sizer X_aryy_ary train_absizeX_trainy_trainX_testy_tests rshuffled_splitr"2sH QSJ#-MNN,FFHaffh'[ uvvxuggaj01L]l+U=L-AW=)5+?F V ++)N)Tg?N)numpyr mlxtend.utilsrrr"r#rr's"#$L4,r#