# Path: LinearRegression/leastsquare.py
# def ls(X, Y):
# def augment(data):
# def predict(X, beta):
# def RSS(X, Y, beta):
# def thegama2(yhat, output, p):
# def z_score(beta, theg, data):
#     XT = np.transpose(X)
#     M = XT.dot(X)
#     DT = np.transpose(data)
#     M = np.linalg.inv(DT.dot(data))


import numpy as np
import ozone_data
import copy
from LinearRegression import leastsquare
        for p,xp in enumerate(x):
            if p not in model.directions:
                continue
            i = model.t_idxes[model.directions.index(p)]
            t = X_train[i][p]
            #print "predict:", i
            x_t, t_x = basis_pair(xp, t)
            if model.xt == True:
                product *= x_t
            else:
                product *= t_x
        h.append(product)
    return np.transpose(beta).dot(np.array(h))

def GCV(Model, Y, lmbda, beta):
    N = len(Y)
    X = model2matix(Model)
    X = np.transpose(np.array(X))
    Y_hat = leastsquare.predict(X, beta)
    rss = np.sum((Y-Y_hat)**2)
    denominator = (1-float(lmbda)/N)**2
    return rss/denominator

def removed_rss(Model, index, Y):
    X = model2matix(Model)
    del X[index]
    X = np.transpose(np.array(X))
    if len(X) <= 1:
        return 999999999., None
    beta = leastsquare.ls(X, Y)

# Path: LinearRegression/leastsquare.py
# def ls(X, Y):
# def augment(data):
# def predict(X, beta):
# def RSS(X, Y, beta):
# def thegama2(yhat, output, p):
# def z_score(beta, theg, data):
#     XT = np.transpose(X)
#     M = XT.dot(X)
#     DT = np.transpose(data)
#     M = np.linalg.inv(DT.dot(data))


# Path: AdditiveModelsTreesRelatedModels/MARS.py
import numpy as np
import ozone_data
import copy
from LinearRegression import leastsquare

        for p,xp in enumerate(x):
            if p not in model.directions:
                continue
            i = model.t_idxes[model.directions.index(p)]
            t = X_train[i][p]
            #print "predict:", i
            x_t, t_x = basis_pair(xp, t)
            if model.xt == True:
                product *= x_t
            else:
                product *= t_x
        h.append(product)
    return np.transpose(beta).dot(np.array(h))

def GCV(Model, Y, lmbda, beta):
    N = len(Y)
    X = model2matix(Model)
    X = np.transpose(np.array(X))
    Y_hat = leastsquare.predict(X, beta)
    rss = np.sum((Y-Y_hat)**2)
    denominator = (1-float(lmbda)/N)**2
    return rss/denominator

def removed_rss(Model, index, Y):
    X = model2matix(Model)
    del X[index]
    X = np.transpose(np.array(X))
    if len(X) <= 1:
        return 999999999., None
    beta = leastsquare.ls(X, Y)

    Y_hat = leastsquare.predict(X, beta)