# k_sample_transform¶

hyppo.ksample.k_sample_transform(inputs, test_type='normal')

Computes a k-sample transform of the inputs.

For $$k$$ groups, this creates two matrices, the first vertically stacks the inputs. In order to use this function, the inputs must have the same number of dimensions $$p$$ and can have varying number of samples $$n$$. The second output is a label matrix the one-hoc encodes the groups. The outputs are thus (N, p) and (N, k) where N is the total number of samples. In the case where the test a random forest based tests, it creates a (N, 1) where the entries are varlues from 1 to $$k$$ based on the number of samples.

Parameters
• inputs (list of ndarray) -- A list of the inputs. All inputs must be (n, p) where n is the number of samples and p is the number of dimensions. n can vary between samples, but p must be the same among all the samples.

• test_type ({"normal", "rf"}, default: "normal") -- Whether to one-hoc encode the inputs ("normal") or use a one-dimensional categorical encoding ("rf").

Returns

• u (ndarray) -- The matrix of concatenated inputs of shape (N, p).

• v (ndarray) -- The label matrix of shape (N, k) ("normal") or (N, 1) ("rf").