dataaspirant-pca-create-pca.py

class convers_pca():
    def __init__(self, no_of_components):
       self.no_of_components = no_of_components
       self.eigen_values = None
       self.eigen_vectors = None

    def transform(self, a):
       return np.dot(a - self.mean, self.projection_matrix.T)

    def inverse_transform(self, a):
       return np.dot(a, self.projection_matrix) + self.mean

    def fit(self, a):
       self.no_of_components = a.shape[1] if self.no_of_components is None else self.no_of_components
       self.mean = np.mean(a, axis=0)

       cov_matrix = np.cov(a - self.mean, rowvar=False)

       self.eigen_values, self.eigen_vectors = np.linalg.eig(cov_matrix)
       self.eigen_vectors = self.eigen_vectors.T

       self.sorted_components = np.argsort(self.eigen_values)[::-1]

       self.projection_matrix = self.eigen_vectors[
       self.sorted_components[
       :self.no_of_components]]self.explained_variance = self.eigen_values[
       self.sorted_components]
       self.explained_variance_ratio = self.explained_variance / self.eigen_values.sum()