Univariate modeler of a series of numbers. The numbers are regarded be sample from 1, 2, 3, 4

unimodeler.py

from scipy.linalg import pinv
from numpy import abs, arange, ones, dot, finfo, mat, vstack
from sympy import Lambda, var
from numpy.testing import assert_allclose

class UniModeler():
    def __init__(self, data=None):
        self.data = data
        self._model = lambda x: 0

    def set_data(self, data):
        self.data = data
        self._model = lambda x: 0

    def get_data(self):
        return self.data

    def estimate_linear(self):
        """y = X*b; b = pinv(X)*y"""
        N = len(self.data)
        design_matrix =  vstack((ones(N), arange(1,N+1))).T
        b = dot(pinv(design_matrix), mat(self.data).T)
        x = var('x')
        self._model = Lambda(x, b[1]*x+b[0])
        
    def estimate(self):
        if self._model is None: 
            return None
        return self.estimate_linear()
    
    def predict(self, x): 
        return self._model(x)
    
    def predict_many(self, xs):
        return [ self.predict(x) for x in xs ]

    
def test_set_data():
    um = UniModeler()
    um.set_data([1, 2, 3])
    assert [1, 2, 3] == um.get_data()
    um.set_data([1])
    assert [1] == um.get_data()

    
def test_predict():
    um = UniModeler()
    um.set_data([1, 2, 3])
    um.estimate()
    small = 100 * finfo('double').eps
    assert abs(0 - um.predict(0)) < small
    assert abs(1 - um.predict(1)) < small
    assert abs(1 - um.predict_many([0, 1])[1]) < small