Description
To make a library of functions that are frequently used for data anlaysis and machine learning tasks
Changes
- lib_ml : cross validation, KMeans
List of Functions and Structs
lib_matrix
1. MatrixDeterminantF :
> determinant_f
x determinant_2
x determinant_3plus
> is_square_matrix
x 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
21. join_matrix
22. make_matrix_string_literal
23. head
24. tail
25. row_to_columns_conversion
26. columns_to_rows_conversion
lib_ml
1. OLS:
> fit
2. BLR:
> fit
> sigmoid
> log_loss
> gradient_descent
> change_in_loss
> predict
3. KNN
> fit
x predict
4. Distance
> distance_euclidean
> distance_manhattan
> distance_cosine
> distance_chebyshev
5. Kmeans
> fit
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. logistic_predict
15. randomize_vector
16. randomize
17. train_test_split_vector_f
18. train_test_split_f
19. correlation
20. std_dev
21. spearman_rank
22. how_many_and_where_vector
23. how_many_and_where
24. z_score
25. one_hot_encoding
26. shape
27. rmse
28. mse
29. mae
30. r_square
31. mape
32. drop_column
33. preprocess_train_test_split
34. standardize_vector_f
35. min_max_scaler
36. float_randomize
37. confuse_me
38. cv
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
> char_count
> frequent_char
> char_replace
1. extract_vowels_consonants
2. sentence_case
3. remove_stop_words
Comparision with Scikit learn's output
- OLS
- BLR
- KNN
- Kmeans
About the author
- Used Python, learning Rust
- Feedback appreciated
- rd2575691@gmail.com
Vibliography ?
- For Rust : Crazcalm's Tech Stack
- For lib_nn : nnfs.io
- Other blogs mentioned inside