def responseDirect(self, model): y = pg.RVector(len(self.x_), model[0]) for i in range(1, self.nc_): y += pg.pow(self.x_, i) * model[i] return y
def response(self, par): """Yield response (function value for given coefficients).""" y = pg.RVector(self.x_.size(), par[0]) for i in range(1, self.nc_): y += pg.pow(self.x_, i) * par[i] return y
def response(self, par): y = pg.RVector(len(self.x_), par[0]) for i in range(1, self.nc_): y += pg.pow(self.x_, i) * par[i] return y
def response( self, par ): ''' the main thing - the forward operator: return f(x) ''' y = g.RVector( self.x_.size(), par[ 0 ] ) for i in range( 1, self.nc_ ): y += g.pow( self.x_, i ) * par[ i ]; return y;
def response(self, par): """ the main thing - the forward operator: return f(x) """ y = g.RVector(self.x_.size(), par[0]) for i in range(1, self.nc_): y += g.pow(self.x_, i) * par[i] return y
def response(self, par): """ the main thing - the forward operator: return f(x) """ y = pg.RVector(len(self.x_), par[0]) for i in range(1, self.nc_): y += pg.pow(self.x_, i) * par[i] return y
def __init__(self, nc, xvec, verbose=False): pg.ModellingBase.__init__(self, verbose) self.x_ = xvec self.nc_ = nc nx = len(xvec) self.regionManager().setParameterCount(nc) self.jacobian().resize(nx, nc) for i in range(self.nc_): self.jacobian().setCol(i, pg.pow(self.x_, i))
def rmswitherr(a, b, err, errtol=1): """Compute (abs-)root mean square of values with error above a threshold""" fi = pg.find(err < errtol) return sqrt(pg.mean(pg.pow(a[fi] - b[fi], 2)))
def test_RVectorFuncts(self): v = pg.RVector(5, 2.0) self.assertEqual(sum(pg.pow(v, 2)), 20) self.assertEqual(sum(pg.pow(v, 2.0)), 20) self.assertEqual(sum(pg.pow(v, v)), 20)
def response(self, par): y = pg.RVector(self.x_.size(), par[0]) for i in range(1, self.nc_): y += pg.pow(self.x_, i) * par[i] return y
def response( self, par ): y = pg.RVector( self.x_.size(), par[ 0 ] ) for i in range( 1, self.nc_ ): y += pg.pow( self.x_, i ) * par[ i ]; return y;