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Obvious, But Useful (Getting the Epsilon-0 Value when the Interaction Enthalpy Is Zero)

Obvious, But Useful (Getting the Epsilon-0 Value when the Interaction Enthalpy Is Zero)

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  This Really Is Kind of Obvious, But …   There’s something very interesting that we can do to obtain values for the epsilon0 parameter. Let’s stay with the case where there is no interaction enthalpy. In that case, we want to find the epsilon0 value that corresponds to the x1 value at a given free energy minimum. Or conversely, given an epsilon0 value, can we identify the x1 where the free energy minimum occurs? Turns out that, for this…

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Object-Oriented for the CVM (Continued), and an Oops!

Object-Oriented for the CVM (Continued), and an Oops!

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Why Shifting to the Object-Oriented Coding Approach REALLY IS Important:   Well, I hate having to admit it. Mud on my face; all that. But I made a pretty significant Whoops! back this last winter when I posted a “Verification and Validation” document (hah!) to arXiv.     The Sad Story of My Previous Ineptitude   Well, there’s nothing like hearing about someone else’s screw-up in order to make us feel better about our own life, so here goes. I’d…

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Looking at Friends and Neighbors: A Node’s Point-of-View (2-D Cluster Variation Method)

Looking at Friends and Neighbors: A Node’s Point-of-View (2-D Cluster Variation Method)

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Looking around from a Node’s Point-of-View:   As we move from procedural code to object-oriented Python for a Cluster Variation Method (CVM) grid, our perspective shifts. We now need to look at the world from a node’s point-of-view. It’s a lot like updating one’s relationship status in Facebook – except that after updating our own (node) status, we need to not only update the values for all of our own configuration variables, but then we need to travel around the…

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Transition to Object-Oriented Python for the Cluster Variation Method

Transition to Object-Oriented Python for the Cluster Variation Method

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The Cluster Variation Method – A Topographic Approach:   Object-oriented programming is essential for working with the Cluster Variation Method (CVM), especially if we’re going to insert a CVM layer into a neural network. The reason is that approaching free energy minima via changing node states requires dealing with node, net, and grid topographies. If we’re going to be at all strategic in moving towards free energy minima, then we can’t just pick nodes at random. We need to know…

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Figuring Out the Puzzle (in a 2-D CVM Grid)

Figuring Out the Puzzle (in a 2-D CVM Grid)

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The Conundrum – and How to Solve It: We left off last week with a bit of a cliff-hanger; a puzzle with the 2-D CVM. (CVM stands for Cluster Variation Method; it’s a more complex form of a free energy equation that I discussed two weeks ago in this blogpost on The Big, Bad, Scary Free Energy Equation (and New Experimental Results); while not entirely unknown, it’s still not very common yet.) We asked ourselves: which of the two grids…

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2-D Cluster Variation Method: Code V&V

2-D Cluster Variation Method: Code V&V

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New Code (Not Released Yet): V&V the Code Before We Play:   Well, my darling, as you gathered from last week’s post, the world has shifted. Up until now, when we were talking about having a new free energy function to use inside a neural network, we had to do “Gedankenexperiments” (German for “thought experiments”). Now, though, there’s working code – and I so LOVE seeing the numbers and graphs come out; teasing it, playing with it … stroking it…

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