stats.py 文件源码

def fit_linear_model(x,y,slope=None):
    '''Linear least squares (LSQ) linear regression (polynomial fit of degree=1)
    Returns:
        m (float) = slope of linear regression line of form (y = m*x + b)
        b (float) = intercept of linear regression line'''

    assert len(x)==len(y), ("Arrays x & Y must be equal length to fit "
                            "linear regression model.")
    if slope == None:
        (m,b) = scipy.polyfit(x,y,deg=1)
    else:
        LSQ = lambda b: np.sum( (y-(slope*x+b))**2.0 )
        res = scipy.optimize.minimize(LSQ,x0=1,bounds=None)
        (m,b) = (slope,res.x[0])
    return (m,b)