simple_ml 0.2.1

Functions required for data analysis and machine learning tasks
Documentation

Description

To make a library of functions that are frequently used for data anlaysis and machine learning tasks

List of Functions and Structs

lib_matrix

1. MatrixDeterminantF : 
    > determinant_f
        x determinant_2
        x determinant_3plus
    > is_square_matrix
    > round_off_f
    > inverse_f
        x identity_matrix
        x zero_matrix

1. dot_product
2. element_wise_operation
3. matrix_multiplication
4. pad_with_zero
5. print_a_matrix
6. shape_changer
7. transpose
8. vector_addition
9. make_matrix_float
10. make_vector_float
11. round_off_f
12. unique_values
13. value_counts
14. is_numerical
15. min_max_f
16. type_of
17. element_wise_matrix_operation
18. matrix_vector_product_f
19. split_vector
20. split_vector_at

lib_ml

1. MultivariantLinearRegression :
    > multivariant_linear_regression
        x generate_score
    > batch_gradient_descent
        x mse_cost_function
    > hash_to_table
        x train_test_split
        x randomize
        
1. coefficient
2. convert_and_impute
3. covariance
4. impute_string
5. mean
6. read_csv
7. root_mean_square
8. simple_linear_regression_prediction
9. variance
10. convert_string_categorical 
11. normalize_vector_f
12. logistic_function_f
13. log_gradient_f 
14. cost_function_f (WIP)
15. gradient_descent 
16. logistic_predict 
17. randomize
18. train_test_split
19. binary_logistic_regression (WIP)

lib_nn

1. LayerDetails :
    > create_weights
    > create_bias
    > output_of_layer

1. activation_leaky_relu
2. activation_relu
3. activation_sigmoid
4. activation_tanh

lib_string

1. StringToMatch :
    > compare_percentage
        x calculate
    > clean_string
        x char_vector
    > compare_chars
    > compare_position
    > fuzzy_subset
        x n_gram
    > split_alpha_numericals (update: acknowledges spaces)
    > char_count
    > frequent_char
    > char_replace

About the author

Vibliography ?