A personal project to implement Data-mining algorithms in python

• sub_series
• cov_series
• create_cov_matrix
• _eigenvalue
• get_eigenvalue
• get_pca
• correlation_series
• correlation_frame
• Tests: test.py

• Gets a pandas series, calculates average (mean) and returns result subtraction of the series and the average
• for example:

sr = [3, 4, 1, 2, 0]
sr.mean = (3+4+1+2+0)/sr.len = 10/5 = 2
result = [3-2, 4-2, 1-2, 2-2, 0-2] = [1, 2, -1, 0, -2]

• Gets to pandas series and returns covariance of them
• Covariance Formula:
  
• Length of sr1 & sr2 must be equal
• for example:

sr1 = pd.Series([3, 4, 1, 2, 0])
sr1 - sr1.mean = 1, 2, -1, 0, -2
sr2 = pd.Series([1, 3, 0, 4, 2])
sr2 - sr2.mean = -1, 1, -2, 2, 0
result = ((1*-1) + (21) + (-1-2) + (02) + (-20)) / sr1.len
          = (-1 + 2 + 2 + 0 + 0) / 5 = 3 / 5 = 0.6

Creates a Covariance Matrix from a matrix (pandas.Dataframe)

Covariance Matrix:

n is number od columns
Cov_ij is Covariance of column_i & column_j

for example:
input matrix:

output:

• Gets a Matrix (pandas.Dataframe) and returns descending sorted eigenvalue & eigenvectors
• This function using numpy.linalg.eigh

1. Gets a matrix (pandas.Dataframe)
2. Transforms it to its Covariance Matrix (using create_cov_matrix)
3. Returns 'eigenvalue' and 'eigenvectors' (using _eigenvalue)

Gets a Matrix (pandas.Dataframe) and returns the PCA

• Gets two pandas.series and calculates their Pearson Correlation
• Pearson Correlation Formula:
  

Gets a matrix (pandas.Dataframe) and returns correlation matrix of its columns