Merge pull request #3 from virtual-labs/testing

Testing

.github/workflows/deployment-script.yml

@@ -18,10 +18,10 @@ jobs:
             node-version: '12'
             check-latest: true
     - run: |
-        git clone -b pipeline-new https://github.com/virtual-labs/ph3-lab-mgmt
+        git clone --depth 1 https://github.com/virtual-labs/ph3-lab-mgmt
         cd ph3-lab-mgmt
         npm install
-        node exp.js
+        npm run build-exp
         cd ../
         git config --local user.email "[email protected]"
         git config --local user.name "vleadadmin"

experiment-descriptor.json

@@ -0,0 +1,55 @@
+{
+  "unit-type": "lu",
+  "label": "",
+  "basedir": ".",
+  "units": [
+    {
+      "unit-type": "aim"
+    },
+    {
+      "target": "theory.html",
+      "source": "theory.md",
+      "label": "Theory",
+      "unit-type": "task",
+      "content-type": "text"
+    },
+
+    {
+      "target": "procedure.html",
+      "source": "procedure.md",
+      "label": "Procedure",
+      "unit-type": "task",
+      "content-type": "text"
+    },
+    {
+      "target": "simulation.html",
+      "source": "simulation/index.html",
+      "label": "Simulation",
+      "unit-type": "task",
+      "content-type": "simulation"
+    },
+	{
+      "target": "observations.html",
+      "source": "observations.md",
+      "label": "Observations",
+      "unit-type": "task",
+      "content-type": "text"
+    },
+    {
+      "target": "assignment.html",
+      "source": "assignment.md",
+      "label": "Assignment",
+      "unit-type": "task",
+      "content-type": "text"
+    },
+
+    {
+      "target": "references.html",
+      "source": "references.md",
+      "label": "References",
+      "unit-type": "task",
+      "content-type": "text"
+    }
+  ]
+}
+

experiment/aim.md

@@ -1 +1,6 @@
-### Aim of the experiment
+The objective of this experiment is to demonstrate different annealing strategies in the solutions to optimization problems using a Hopfield model. This is illustrated using the weighted matching problem as a case study.
+
+The optimization is guided by the activation dynamics of a Hopfield network. The activation dynamics is guided by the energy surface defined by the activation states of the units in the Hopfield network. The three relaxation strategies studied are:
+(a) Deterministic relaxation
+(b) Stochastic relaxation
+(c) Mean-field approximation

experiment/assignment.md

@@ -0,0 +1,5 @@
+1. What is a local minima problem in optimization ?
+2. How is mean-field annealing applied in the solution of optimization problems?
+3. Discuss the solution to the Traveling salesman problem using deterministic relaxation and stochastic relaxation.
+**Hint**: Refer [Yegnanarayana, 1999, pg. 299] and [Wilson and Pawley, 1988]
+

experiment/experiment-name.md

@@ -1 +1 @@
-## Experiment name
+## Deterministic, Stochastic and Mean-field Annealing of Hopfield Models

experiment/observations.md

@@ -0,0 +1,5 @@
+- Deterministic relaxation can get stuck in local minima depending on the starting point.
+- Stochastic relaxation uses simulated annealing at different temperatures with probablistic update, which can help the network get out of the local minima, to settle for a deeper minima.
+- Stochastic relaxation can take time to reach equilibrium.
+- Mean-field annealing is used to speed up the process.
+

experiment/procedure.md

@@ -1 +1,7 @@
-### Procedure
+- Go through the example presented, so as to understand the operation being done in the experiment to optimize the weighted matching problem.
+- Select the type of relaxation.
+- Select a graph type with given number of nodes along with the location of the nodes in a 2-D plane.
+- Click on 'Init' to initialize the graph.
+- After observing the set of points generated and the equations used to optimize the weighted graph, click on 'Anneal' to start annealing the network.
+- Go through the output of the problem and the output states of nodes after each update.
+

experiment/references.md

@@ -1 +1,11 @@
-### Link your references in here
+- B. Yegnanarayana, Artificial Neural Networks, New Delhi, India : Prentice-Hall of India, p. 293, 1999.
+- C. Peterson and B. Soderberg, "Neural optimization", in The Handbook of Brain Theory and Neural Networks (M.A. Arbib, ed.), Cambridge, MA: MIT Press, pp. 617-621, 1995.
+- J.A. Hertz, A. Krogh, and R.G. Palmer, Introduction to the Theory of Neural Computation, New York: Addison-Wesley, 1991.
+- B. Muller and J. Reinhardt, Neural Networks: An Introduction, Physics of Neural Networks, New York: Springer-Verlag, 1991.
+- A.L. Yuille, "Constrained optimization and the elastic net", in The Handbook of Brain Theory and Neural Networks (M.A. Arbib, ed.), Cambridge, MA. MIT Press, pp. 250-255, 1995.
+- S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, 1984.
+- N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller, "Equation of state calculations by fast computing machines", J. Chem. Phy., vol. 21, no. 6, pp. 1087-1092, 1953.
+- C. Peterson and J.R. Anderson, "A mean field theory learning algorithm for neural networks", Complex Systems, .vol. 1, pp. 995-1019, 1987.
+- R.J. Glauber, "Time-dependent statistics of the Ising model", J. Math. Phys., vol. 4, pp. 294-307, 1963.
+- S. Haykin, Neural Networks: A Comprehensive Foundation, New York: Macmillan College Publishing Company Inc., 1994.
+