Homework numpy

hw_numpy/README.md

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+Script *numpy.challenge* creates 3 numpy arrays in 3 different ways and implements several functions using **numpy** library:
+
+1. matrix_multiplication() - takes 2 matrices, returns their matrix multiplication;
+2. multiplication_check() - takes list of matrixes, returns True if they can be multiplied, otherwise returns False;
+3. multiply_matrices() - takes list of matrixes, returns their matrix multiplication if they can be multiplied;
+4. compute_2d_distance() - takes 2 1-dimensional arrays, returns distance between them;
+5. compute_multidimensional_distance() - takes 2 1-dimensional arrays, returns distance between them;
+6. compute_pair_distances() - takes matrix, returns matrix of pair distances for its rows.

hw_numpy/numpy_challenge.py

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+import numpy as np
+
+if __name__ == "__main__":
+    # Creating an array from a list(the first way):
+    array1 = np.array([6, 6, 6])
+    # Creating an array with arange (the second way):
+    array2 = np.arange(6)
+    # Creating an array of zeros (the third way):
+    array3 = np.zeros(6)
+
+
+# This function computes matrix multiplication of 2 matrices
+def matrix_multiplication(matrix1, matrix2):
+    return np.matmul(matrix1, matrix2)
+
+# This function checks whether multiple matrices can be multiplied
+def multiplication_check(*matrices):
+    for i in range(len(matrices) - 1):
+        if matrices[i].shape[1] != matrices[i+1].shape[0]:
+            return False
+    return True
+
+# This function computes matrix multiplication of several matrices (if possible)
+def multiply_matrices(*matrices):
+    if multiplication_check(*matrices):
+        return np.linalg.multi_dot(matrices)
+    else:
+        return None
+
+# This function computes distance between 2 point in 2-dimensional space     
+def compute_2d_distance(array1, array2):
+    return np.linalg.norm(array2 - array1)
+
+# This function computes distance between 2 point in multidimensional space        
+def compute_multidimensional_distance(array1, array2):
+    return np.linalg.norm(array2 - array1)
+
+# This function computes matrix of pair distances between rows of input matrix
+def compute_pair_distances(matrix):
+    dim = matrix.shape[0] # amount of rows in input matrix
+    pair_distances = np.zeros((dim, dim))
+    for n_row in range(dim):
+        for n_col in range(dim):
+            if n_row != n_col: # filling matrix of pair distances
+                pair_distances[n_row, n_col] = np.linalg.norm(matrix[n_row] - matrix[n_col])
+    return pair_distances