def generate(self, params, pivot, settings):
    '\n      Generates a synthetic history from fitted parameters.\n      @ In, params, dict, characterization such as otained from self.characterize()\n      @ In, pivot, np.array(float), pivot parameter values\n      @ In, settings, dict, additional settings specific to algorithm\n      @ Out, synthetic, np.array(float), synthetic estimated model signal\n    '
    from sklearn.preprocessing import PolynomialFeatures
    synthetic = np.zeros((len(pivot), len(params)))
    degree = settings['degree']
    features = PolynomialFeatures(degree=degree)
    xp = features.fit_transform(pivot.reshape((- 1), 1))
    for (t, (target, _)) in enumerate(params.items()):
        model = params[target]['model']['object']
        synthetic[:, t] = model.predict(xp)
    return synthetic
Generates a synthetic history from fitted parameters.
@ In, params, dict, characterization such as otained from self.characterize()
@ In, pivot, np.array(float), pivot parameter values
@ In, settings, dict, additional settings specific to algorithm
@ Out, synthetic, np.array(float), synthetic estimated model signal
def generate(self, params, pivot, settings):
    '\n      Generates a synthetic history from fitted parameters.\n      @ In, params, dict, characterization such as otained from self.characterize()\n      @ In, pivot, np.array(float), pivot parameter values\n      @ In, settings, dict, additional settings specific to algorithm\n      @ Out, synthetic, np.array(float), synthetic estimated model signal\n    '
    from sklearn.preprocessing import PolynomialFeatures
    synthetic = np.zeros((len(pivot), len(params)))
    degree = settings['degree']
    features = PolynomialFeatures(degree=degree)
    xp = features.fit_transform(pivot.reshape((- 1), 1))
    for (t, (target, _)) in enumerate(params.items()):
        model = params[target]['model']['object']
        synthetic[:, t] = model.predict(xp)
    return synthetic