Cara menggunakan combinations of array python

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Slices:

Slices are objects so they can be stored in variables. Some data structures allow for indexing and slicing such as lists, strings, and tuples.
We can use integers to specify the upper and lower bound of the slice or use a slice object.

s = slice(4,8)
lst = [1, 3, 'w', '3', 'r', 11, 16]
text = 'w3resource'
tpl = (2,4,6,8,10,12,14)
print(lst[s])
print(text[s])
print(tpl[s])

Output:

['r', 11, 16]
sour
(10, 12, 14)

The slice s represents a slice from the fourth element to the sixth element. We apply the same slice object to a list, string, and tuple.

Given an array of distinct integers candidates and a target integer target, return a list of all unique combinations of candidates where the chosen numbers sum to target. You may return the combinations in any order.

The same number may be chosen from candidates an unlimited number of times. Two combinations are unique if the of at least one of the chosen numbers is different.

The test cases are generated such that the number of unique combinations that sum up to target is less than 150 combinations for the given input.

This initially creates clusters of points normally distributed (std=1) about vertices of an n_informative-dimensional hypercube with sides of length 2*class_sep and assigns an equal number of clusters to each class. It introduces interdependence between these features and adds various types of further noise to the data.

Without shuffling, X horizontally stacks features in the following order: the primary n_informative features, followed by n_redundant linear combinations of the informative features, followed by n_repeated duplicates, drawn randomly with replacement from the informative and redundant features. The remaining features are filled with random noise. Thus, without shuffling, all useful features are contained in the columns X[:, :n_informative + n_redundant + n_repeated].

Read more in the .

The number of samples.

The total number of features. These comprise n_informative informative features, n_redundant redundant features, n_repeated duplicated features and n_informative1 useless features drawn at random.

The number of informative features. Each class is composed of a number of gaussian clusters each located around the vertices of a hypercube in a subspace of dimension n_informative. For each cluster, informative features are drawn independently from N(0, 1) and then randomly linearly combined within each cluster in order to add covariance. The clusters are then placed on the vertices of the hypercube.

The number of redundant features. These features are generated as random linear combinations of the informative features.

The number of duplicated features, drawn randomly from the informative and the redundant features.

The number of classes (or labels) of the classification problem.

The number of clusters per class.

The proportions of samples assigned to each class. If None, then classes are balanced. Note that if n_informative3, then the last class weight is automatically inferred. More than n_informative4 samples may be returned if the sum of n_informative5 exceeds 1. Note that the actual class proportions will not exactly match n_informative5 when n_informative7 isn’t 0.

The fraction of samples whose class is assigned randomly. Larger values introduce noise in the labels and make the classification task harder. Note that the default setting flip_y > 0 might lead to less than n_informative8 in y in some cases.

The factor multiplying the hypercube size. Larger values spread out the clusters/classes and make the classification task easier.

If True, the clusters are put on the vertices of a hypercube. If False, the clusters are put on the vertices of a random polytope.

Shift features by the specified value. If None, then features are shifted by a random value drawn in [-class_sep, class_sep].

Multiply features by the specified value. If None, then features are scaled by a random value drawn in [1, 100]. Note that scaling happens after shifting.

Shuffle the samples and the features.

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See .