I am using hdbscan function under the package called "dbscan" to perform clustering on a data. I am not able to predict the membership of a new data point after the cluster is built. The predict function works for the object built under dbscan function but it is not working for hdbscan. The error is:
"Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "hdbscan""
Any suggestion?
As far as I know, there is no predict method. In HDBSCAN there is a approximate_predict() function.
How this works you can check more on the documentation.
Related
I'm making the interface package which can input the parameters of the models in the simulation loop.
To connect between the interface package and the simulation model, I used the Controlbus from the Standard Modelica Library Ver. 3.2.2.
Checking model was Okay, but if i simulate the model, the error like the picture below popped up.
And here's the equation related to this model
Omega_e = Omega_d * N_t[N];
Alpha_d = der(Omega_d);
To solve the differential equation, i think the solver need a specific parameter of N_t.
So i put the parameters from the interface model and sent the parameters using the Controlbus component in the Standard Modelica Library.
As in the picture above, i definitely put the parameters.
(Specific values of the parameters are deleted because it's a confidential)
I can't find what is the problem of this error.
Please help me guys.
Thank you very much.
Based on the incomplete model it's a bit tricky to say what happened, but:
Sending parameters through the control-bus (or a connector in general) is a bit complicated and not that encouraged.
It should be possible by declaring the "computed parameter" as parameter Integer N(fixed=false); initial equation N=myBus.N;, and don't have it as parameter in the connector.
If you don't declare it as parameter Dymola will try (and fail) to differentiate it.
If you declare it as parameter in the connector it will not be propagated (as connecting two parameters lead to an assertion).
This is my sample network and idea of trying how to make a new routing instead of just the shortest path (the path i want to follow is via the pink arrows)
What am I missing here to make my predefined function work?
Fixing the basics
As explained here, you can't instantiate a Java List, because it is an interface. You can however instantiate any implementing Class of a List, for example an ArrayList.
With this in mind your code will look like this:
List<Path> myPath = new ArrayList<Path>();
myPath.add(path14);
myPath.add(path8);
myPath.add(path);
myPath.add(path1);
myPath.add(path4);
myPath.add(path13);
return myPath;
So far for the basics.
Where to go from here
To get it to consider your actual source and destination for the route planning, define both as input parameters of type ILocation in the properties of the function.
Now comes the really tricky part: writing your own or importing a routing algorithm that can give you that list of paths automatically based on criteria that you define. This is however a topic too broad for this question. The basic steps will be:
Create a graph that represents your AnyLogic path network
Solve the graph routing problem with a solving algorithm (eg. Dijkstra Algorithm), using the graph, the startpoint and the endpoint
Convert the solution you get from the solver back again to an ArrayList that you can work with in AnyLogic
You can do these steps on your own, eg. by implementing the Dijkstra Algorithm yourself, or you import into AnyLogic one of the available graph solving Java packages like JUNG or Graphhopper. In this article I explain step by step how to do so with JUNG.
I would like to create my own operations to merge networks. So I've taken a look to the code, and I modified engine/topology.py to create my new operation.
I didn't modified layers/wrappers.py because it's only for RNN and when I modify it I get an error.
Are there other files/classes to modify? Don't I have to do something else somewhere else to specify what to do during the backward pass?
You don't have to change any other files if you have implemented your operation properly with backend operations only. The backend is clever and takes care of the computation of the gradients for the backpropagation by itself.
This means that all parameters that will change over time have to be defined with K.variable, and you only use mathematical operations defined in keras.backend. Otherwise the backend will not be able to perform the backpropagation properly.
Side-note: Instead of modifying the source code of keras, you could implement your own class that extends the Merge class and override the call function for your custom operation.
Say, I've UNITs 1,2,3,4 ( either as Model reference or Subsystem) for which I've units tests ready using matlab.unittest.TestCase framework.
What could be the easiest way to write integration test fro entire system ?
I need some way to set Global_Inputx ( x = 1,2,3 ) and verify Global_Outy ( y =1,2 ) in easiest possible way (may be utilizing the Unit tests) ?
I can use Matlab 14a
PS: I've already gone through this but it didn't help.
I think the question of integration testing in Simulink is a complex one that may involve formal methods like code and coverage analysis of the dynamic system under test, automatic test generation e.t.c. If you haven't already, you may want to check out "Verification, Validation and Test" section of the MathWorks product line up: http://www.mathworks.com/products/?s_tid=gn_ps.
However to answer your specific question of how you would set the global input and verify global output in your test:
Depending on where your global input data that feeds the inport blocks resides (MATLAB base workspace, model workspace, e.t.c), do you think you could set the external input of the model to that data. For example:
set_param(, 'ExternalInput', )
This could be defined in your test class setup, test method setup or in the test depending on when the data is available and where defining it is appropriate. The data could also be passed in directly to the sim() command. Parameterized testing (http://www.mathworks.com/help/matlab/matlab_prog/create-basic-parameterized-test.html) is an option to consider if you want to test the system with different sets of inputs. The external input value becomes the parameter in this context.
If you have your model set up for output logging, then once the simulation is done, you would get the logged outputs, which you could then compare against a baseline.
Does that help? or am I way off the base here. If you can add more details, I can try again.
This method is working totally right in matlab. but, when I compiled it in a deployment (DLL files) the DLL can't call it. I think that because private function and these issues. Can any one help me?
I want to call it from the DLL or any equivalence method that will act the same. the tree is from type Compacttreebagger .. thank you
if the passed tree to the function predict is from class CompactTreeBagger then you should put this class definition to the help resources of your deployment tool otherwise you will get an error that the function predict is not defined to take integer parameter.