# MetaModel ComponentΒΆ

A number of utility components come packaged with OpenMDAO. MetaModel is one that lets you quickly create a component with surrogate models used to compute the outputs based on training data. You can set up a MetaModel instance with as many parameters and outputs as you like, and you can also use a different surrogate model for each output.

Note

What’s the difference between a MetaModel and a surrogate model? In OpenMDAO, “surrogate model” refers to the model for a single response, and MetaModel represents a collection of surrogate models trained at the same locations in the design space.

This code will set up a really simple Group with only a single MetaModel instance, using one parameter and two outputs.

```
from __future__ import print_function
import sys
import numpy as np
from openmdao.api import Group, Component, MetaModel, KrigingSurrogate, FloatKrigingSurrogate
class TrigMM(Group):
''' FloatKriging gives responses as floats '''
def __init__(self):
super(TrigMM, self).__init__()
# Create meta_model for f_x as the response
sin_mm = self.add("sin_mm", MetaModel())
sin_mm.add_param('x', val=0.)
sin_mm.add_output('f_x:float', val=0., surrogate=FloatKrigingSurrogate())
sin_mm.add_output('f_x:norm_dist', val=(0.,0.), surrogate=KrigingSurrogate(eval_rmse=True))
```

Since we want the ‘f_x:norm_dist’ to calculate both the norm and error, we need to set the optional eval_rmse argument to True.

Now we’ll setup the problem and set some training data. Here we just generate the data on the fly, but normally you would have pre-generated this data and then would just import it and use it.

```
from openmdao.api import Problem
prob = Problem()
prob.root = TrigMM()
prob.setup()
#traning data is just set manually. No connected input needed, since
# we're assuming the data is pre-existing
prob['sin_mm.train:x'] = np.linspace(0,10,20)
prob['sin_mm.train:f_x:float'] = np.sin(prob['sin_mm.train:x'])
prob['sin_mm.train:f_x:norm_dist'] = np.cos(prob['sin_mm.train:x'])
prob['sin_mm.x'] = 2.1 #prediction happens at this value
prob.run()
print('float predicted:', '%3.4f'%prob['sin_mm.f_x:float']) #predicted value
print('float actual: ', '%3.4f'%np.sin(2.1))
print('norm_dist predicted:', '%3.4f,'%prob['sin_mm.f_x:norm_dist'][0], '%3.4e'%prob['sin_mm.f_x:norm_dist'][1]) #predicted value
print('norm_dist actual: ', '%3.4f'%np.cos(2.1))
```

You should get some output that looks like this:

```
float predicted: 0.8632
float actual: 0.8632
norm_dist predicted: -0.5048, ...
norm_dist actual: -0.5048
```

Notice that one of the outputs is non-float data. Some surrogate models (like Kriging), can return non-float data like integers, strings, or probability distributions.