budiajimatrix 1.0.0

Library pengolahan matrix lengkap untuk Python - cocok untuk time series, GSTAR, VARIMA, dan analisis statistik

BudiajiMatrix

BudiajiMatrix adalah library Python untuk pengolahan matrix yang lengkap dan mudah digunakan. Cocok untuk keperluan analisis time series, GSTAR, VARIMA, dan berbagai analisis statistik lainnya.

🚀 Fitur Utama

• Operasi Dasar: Penjumlahan, pengurangan, perkalian, pembagian matrix
• Dekomposisi: Cholesky, QR, LU, SVD, Eigendecomposition, Schur
• Nilai Karakteristik: Determinant, trace, rank, norm, condition number, eigenvalues, eigenvectors
• Properti Matrix: Cek simetris, positif definit, orthogonal, singular, diagonal
• Statistik: Mean, variance, standard deviation, covariance, correlation
• Normalisasi: Normalisasi baris/kolom, standardisasi
• Solving Systems: Linear system solver, least squares
• Matrix Power: Pangkat, exponential, logarithm, square root
• Dan banyak lagi!

📦 Instalasi

pip install budiajimatrix

💡 Cara Penggunaan

Import Library

import numpy as np
from budiajimatrix import transpose, multiply, eigenvalues, inverse, covariance

Contoh Penggunaan Dasar

import numpy as np
from budiajimatrix import transpose, multiply, add

# Buat matrix
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

# Transpose
print("Transpose A:")
print(transpose(A))
# Output:
# [[1 3]
#  [2 4]]

# Perkalian matrix
print("A × B:")
print(multiply(A, B))
# Output:
# [[19 22]
#  [43 50]]

# Penjumlahan
print("A + B:")
print(add(A, B))
# Output:
# [[ 6  8]
#  [10 12]]

Eigenvalues dan Eigenvectors

from budiajimatrix import eigenvalues, eigenvectors

A = np.array([[4, 2], [1, 3]])

# Hitung eigenvalues (akar ciri)
eig_vals = eigenvalues(A)
print("Eigenvalues:", eig_vals)

# Hitung eigenvectors (vektor ciri)
eig_vecs = eigenvectors(A)
print("Eigenvectors:")
print(eig_vecs)

Dekomposisi Matrix

from budiajimatrix import svd, qr_decomposition, cholesky

# Singular Value Decomposition
A = np.array([[1, 2], [3, 4], [5, 6]])
U, S, Vt = svd(A)
print("U:", U)
print("S:", S)
print("Vt:", Vt)

# QR Decomposition
A = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]])
Q, R = qr_decomposition(A)
print("Q:", Q)
print("R:", R)

# Cholesky (untuk matrix positif definit)
A = np.array([[4, 12], [12, 37]])
L = cholesky(A)
print("L:", L)

Statistik Matrix

from budiajimatrix import covariance, correlation, mean, std

# Data matrix (observasi × variabel)
data = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9],
                 [10, 11, 12]])

# Covariance matrix
cov_matrix = covariance(data)
print("Covariance Matrix:")
print(cov_matrix)

# Correlation matrix
corr_matrix = correlation(data)
print("Correlation Matrix:")
print(corr_matrix)

# Mean per kolom
print("Mean:", mean(data, axis=0))

# Standard deviation per kolom
print("Std:", std(data, axis=0))

Normalisasi dan Standardisasi

from budiajimatrix import standardize, normalize_rows

data = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])

# Standardisasi (mean=0, std=1)
standardized = standardize(data)
print("Standardized:")
print(standardized)

# Normalisasi per baris
normalized = normalize_rows(data)
print("Normalized Rows:")
print(normalized)

Properti Matrix

from budiajimatrix import is_symmetric, is_positive_definite, rank, determinant

A = np.array([[2, 1], [1, 2]])

# Cek simetris
print("Symmetric?", is_symmetric(A))

# Cek positif definit
print("Positive Definite?", is_positive_definite(A))

# Rank
print("Rank:", rank(A))

# Determinant
print("Determinant:", determinant(A))

Solving Linear Systems

from budiajimatrix import solve_linear_system, least_squares

# Solve Ax = b
A = np.array([[3, 1], [1, 2]])
b = np.array([9, 8])
x = solve_linear_system(A, b)
print("Solution x:", x)

# Least squares (untuk overdetermined system)
A = np.array([[1, 1], [1, 2], [1, 3]])
b = np.array([2, 3, 4])
x = least_squares(A, b)
print("Least Squares Solution:", x)

📚 Dokumentasi Lengkap

Operasi Dasar

• add(matrix1, matrix2) - Penjumlahan matrix
• subtract(matrix1, matrix2) - Pengurangan matrix
• multiply(matrix1, matrix2) - Perkalian matrix
• element_multiply(matrix1, matrix2) - Perkalian elemen per elemen
• scalar_multiply(matrix, scalar) - Perkalian dengan skalar
• divide(matrix, scalar) - Pembagian dengan skalar

Transformasi

• transpose(matrix) - Transpose matrix
• inverse(matrix) - Inverse matrix
• pseudo_inverse(matrix) - Moore-Penrose pseudo-inverse
• conjugate(matrix) - Konjugat matrix
• adjoint(matrix) - Adjoint matrix

Dekomposisi

• cholesky(matrix) - Dekomposisi Cholesky
• qr_decomposition(matrix) - Dekomposisi QR
• lu_decomposition(matrix) - Dekomposisi LU
• svd(matrix) - Singular Value Decomposition
• eigendecomposition(matrix) - Dekomposisi Eigen
• schur(matrix) - Dekomposisi Schur

Nilai Karakteristik

• determinant(matrix) - Determinan
• trace(matrix) - Trace
• rank(matrix) - Rank
• norm(matrix, ord) - Norm matrix
• condition_number(matrix) - Condition number
• eigenvalues(matrix) - Eigenvalues (akar ciri)
• eigenvectors(matrix) - Eigenvectors (vektor ciri)

Properti Matrix

• is_symmetric(matrix) - Cek simetris
• is_positive_definite(matrix) - Cek positif definit
• is_orthogonal(matrix) - Cek orthogonal
• is_singular(matrix) - Cek singular
• is_diagonal(matrix) - Cek diagonal

Matrix Khusus

• identity(n) - Identity matrix n×n
• zeros(shape) - Matrix nol
• ones(shape) - Matrix satu
• diagonal(values) - Diagonal matrix
• random_matrix(shape, distribution) - Random matrix

Statistik

• mean(matrix, axis) - Mean
• variance(matrix, axis) - Variance
• std(matrix, axis) - Standard deviation
• covariance(matrix) - Covariance matrix
• correlation(matrix) - Correlation matrix

Operasi Baris/Kolom

• row_sum(matrix) - Jumlah per baris
• col_sum(matrix) - Jumlah per kolom
• row_mean(matrix) - Mean per baris
• col_mean(matrix) - Mean per kolom
• normalize_rows(matrix) - Normalisasi per baris
• normalize_cols(matrix) - Normalisasi per kolom
• standardize(matrix) - Standardisasi matrix

Solving Systems

• solve_linear_system(A, b) - Solve Ax = b
• least_squares(A, b) - Least squares solution

Matrix Power

• matrix_power(matrix, n) - Matrix pangkat n
• matrix_exp(matrix) - Matrix exponential
• matrix_log(matrix) - Matrix logarithm
• matrix_sqrt(matrix) - Matrix square root

Distance/Similarity

• frobenius_distance(matrix1, matrix2) - Jarak Frobenius
• cosine_similarity(vector1, vector2) - Cosine similarity

🛠️ Requirements

• Python >= 3.7
• NumPy >= 1.18.0
• SciPy >= 1.4.0

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Budi Aji

🤝 Contributing

Contributions, issues, and feature requests are welcome!

⭐ Show your support

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