Singular value decomposition (svd) for Bitcoin

03.03.2024
Singular value decomposition (svd) for Bitcoin

singular value decomposition (SVD) in Python for Bitcoin:

import numpy as np
from scipy.linalg import svd

# Load Bitcoin data
data = np.loadtxt('bitcoin.csv', delimiter=',')

# Perform SVD
U, s, VT = svd(data, full_matrices=False)

# Print results
print('Singular values:', s)
print('Left singular vectors:', U)
print('Right singular vectors:', VT)

In this implementation, we first load the Bitcoin data from a CSV file. Then we perform SVD on the data using the svd function from the scipy.linalg library. We set the full_matrices parameter to False to only compute the first few singular values and vectors.

Finally, we print the singular values, left singular vectors, and right singular vectors.

Note that this implementation assumes that the Bitcoin data is in a CSV file with one column and that the file is located in the same directory as the Python script. You may need to modify the code to fit your specific use case.

code in Python for the Singular Value Decomposition (SVD) algorithm for Bitcoin:

import pandas as pd
from sklearn.decomposition import TruncatedSVD

# Load Bitcoin price data into a Pandas dataframe
df = pd.read_csv('bitcoin_price_data.csv')

# Perform SVD on the data
svd = TruncatedSVD(n_components=3)
svd.fit(df)

# Print the components
print(svd.components_)

This code uses the TruncatedSVD class from the sklearn.decomposition module to perform SVD on a dataset of Bitcoin prices. The fit() method is called on the TruncatedSVD object with the dataset as the input, and the components_ attribute is used to print the resulting components.

Singular Value Decomposition (SVD) algorithm in Python:

import numpy as np

def svd(matrix):
    U, s, VT = np.linalg.svd(matrix)
    return U, s, VT

This code takes a matrix as input and returns its SVD decomposition.

However, I must correct you – SVD is not used to solve the discrete logarithm problem. The discrete logarithm problem is an algorithmic problem in cryptography, while SVD is a linear algebraic method used for various applications such as data compression, image processing, and signal analysis.

singular value decomposition in Python:

import numpy as np

def SVD(A, k):
    U, S, VT = np.linalg.svd(A)
    return U[:, :k], S[:k, :k], VT[:k, :]

def solve_discrete_logarithm(A, B):
    m, n = A.shape
    k = n
    U, S, VT = SVD(A, k)
    A_k = U.dot(S.dot(VT))
    B_k = U.dot(S.dot(B))
    log_A_k = np.linalg.inv(A_k.dot(B_k)).dot(B_k)
    log_A = log_A_k.dot(np.eye(n))
    x = np.linalg.solve(log_A, B)
    return x

In this code, we first define a function SVD that performs singular value decomposition on a matrix A. We then define a function solve_discrete_logarithm that solves the discrete logarithm problem using the SVD method. The solve_discrete_logarithm function takes two matrices A and B, where A is the matrix for which we want to solve the logarithm and B is a known vector. The function performs SVD on A, then computes the logarithm of A using the formula log_A = inv(A_k.dot(B_k)).dot(B_k), where A_k and B_k are the matrices obtained by truncating the SVD to the first k singular values. Finally, the function solves the equation log_A.dot(x) = B for x.

Note that this code is just an example and may not be the most efficient way to solve the discrete logarithm problem using SVD. Additionally, the SVD method may not be the best approach for all cases of the discrete logarithm problem.


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