# `quantitative` ## `euclidean_dist_matrix` ``` Calculates the Euclidean distance matrix for a data matrix using SciPy. Parameters (inputs) ---------- X: a Pandas or Polars DataFrame or a NumPy array. It represents a data matrix. Returns (outputs) ------- M: the Euclidean distance matrix between the rows of `X`. ``` ## `euclidean_dist` ``` Calculates the Euclidean distance between a pair of vectors. Parameters (inputs) ---------- xi, xr: a pair of Pandas or Polars Series or DataFrames, or Numpy arrays. They represent a couple of statistical observations of quantitative variables. Returns (outputs) ------- The Euclidean distance between the observations `xi` and `xr`. ``` ## `minkowski_dist_matrix` ``` Calculates the Minkowski distance matrix for a data matrix using SciPy. Parameters (inputs) ---------- X: a Pandas or Polars DataFrame or a NumPy array. It represents a data matrix. q: the parameters that defines the Minkowski form. Some particular cases: q=1 := Manhattan, q=2 := Euclidean. Returns (outputs) ------- M: the Minkowski(`q`) distance matrix between the rows of `X`. ``` ## `minkowski_dist` ``` Calculates the Minkowski distance between a pair of vectors. Parameters (inputs) ---------- xi, xr: a pair of quantitative vectors. They represent a couple of statistical observations. q: the parameters that defines the Minkowski form. Some particular cases: q=1 := Manhattan, q=2 := Euclidean. Returns (outputs) ------- The Minkowki(`q`) distance between the observations `xi` and `xr`. ``` ## `canberra_dist_matrix` ``` Calculates the Canberra distance matrix for a data matrix using SciPy. Parameters (inputs) ---------- X: a pandas/polars DataFrame or a NumPy array. It represents a data matrix. Returns (outputs) ------- M: the Canberra distance matrix between the rows of `X`. ``` ## `canberra_dist` ``` Calculates the Canberra distance between a pair of vectors. Parameters (inputs) ---------- xi, xr: a pair of quantitative vectors. They represent a couple of statistical observations. Returns (outputs) ------- The Canberra distance between the observations `xi` and `xr`. ``` ## `pearson_dist_matrix` ``` Calculates the Pearson distance matrix for a data matrix using SciPy. Parameters (inputs) ---------- X: a pandas/polars DataFrame or a NumPy array. It represents a data matrix. Returns (outputs) ------- M: the Pearson distance matrix between the rows of X. ``` ## `mahalanobis_dist_matrix` ``` Calculates the Mahalanobis distance matrix for a data matrix using SciPy. Parameters (inputs) ---------- X: a pandas/polars DataFrame or a NumPy array. It represents a data matrix. Returns (outputs) ------- M: the Mahalanobis distance matrix between the rows of X. ``` ## `mahalanobis_dist` ``` Calculates the Mahalanobis distance between a pair of vectors. Parameters (inputs) ---------- xi, xr: a pair of quantitative vectors. They represent a couple of statistical observations. S: the covariance matrix of the data matrix to which `xi` and `xr` belong. Returns (outputs) ------- The Mahalanobis distance between the observations `xi` and `xr`. ``` ## `robust_mahalanobis_dist_matrix` ``` Calculates the Robust Mahalanobis distance matrix for a data matrix `X` using SciPy and a robust estimation of the covariance matrix. Parameters (inputs) ---------- X: a pandas/polars DataFrame or a NumPy array. It represents a data matrix. S_robust: the robust covariance matrix of `X`. Returns (outputs) ------- M: the Robust Mahalanobis distance matrix between the rows of X. ``` ## `robust_mahalanobis_dist` ``` Calculates the Robust Mahalanobis distance between a pair of vectors. Parameters (inputs) ---------- xi, xr: a pair of quantitative vectors. They represent a couple of statistical observations. S_robust: the robust covariance matrix of the data matrix to which `xi` and `xr` belong. Returns (outputs) ------- The Robust Mahalanobis distance between the observations `xi` and `xr`. ```