Edge_intelligence packages and examples

examples/edge_intelligence/README.md

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+# Edge Intelligence Models
+
+This folder contains [PFA](http://dmg.org/pfa/docs/motivation/), [scikit-learn](http://scikit-learn.org/) and [TensorFlow](https://www.tensorflow.org/) and RuleEdgeComponent examples.
+Trained PFA, scikit-learn and TensorFlow models are present under the trained_models directory.
+The generated data is using the [TI SensorTag](http://www.ti.com/ww/en/wireless_connectivity/sensortag/) which has been used for training the windmill model.
+It provides a basic example which uses TI SensorTag data to simulate a sample windmill model. The basic metrics we collect from Windmill model using TI SensorTag are: 
+  - RPM 
+  - Vibration
+  - Ambient Temperature 
+  - Relative Humidity.
+
+The files ```windmill_tf_train.csv``` and ```windmill_tf_test.csv``` in trained_models directory are used for training and testing of accuracy of the TensorFlow model respectively. 
+The file ```windmill_tf_model.py``` in trained_models directory is imdependent of Liota and used for creating the trained TensorFlow model and testing it's accuracy and prediction. 
+
+# RuleEdgeComponent
+
+Rule edge component is one of the edge intelligence components provided by Liota, you can define a simple rule depending on your requirement as a [python lambda](https://www.python-course.eu/lambda.php) function and the rule edge component will process the incoming metrics and the result can be passed onto actuator on basis of the rule you have defined as lambda function.
+
+Currently tested for one metric only.
+
+# Example
+If you have to take rpm as a metric and if rpm exceeds certain limits you have to perform some action, you can define a lambda function as:
+```
+Rule = lambda rpm : 1 if (rpm>=rpm_limit) else 0
+```
+Now just create the ruleEdge and pass it onto RuleEdgeComponent along with the actuator_udm and exceed_limit.
+actuator_udm can be used to pass on the value to the actuator, as of now we are printing them, here udm stands for user defined method.
+exceed_limit is a parameter which can be specified about after how many times the limit is exceeded then action should be taken.
+Example: If rpm exceeds rpm_limit consecutively exceed 3 times then only action should be applied, in that case we would assign exceed_limit = 3
+```
+edge_component = RuleEdgeComponent(ModelRule,exceed_limit,actuator_udm=action_actuator)
+```
+After this register the edge_component and start_collecting the values from sensor.
+The values which will get published to RuleEdge component. Please refer the example file windmill_graphite_rule.py.

examples/edge_intelligence/trained_models/analytics_model.pfa

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+{"@": "PrettyPFA document", "name": "Engine_1", "input": "double", "output": "double", "method": "map", "action": [{"@": "PrettyPFA line 14", "let": {"baseLL": {"@": "PrettyPFA line 14", "prob.dist.gaussianLL": ["input", {"@": "PrettyPFA line 14", "cell": "baseline", "path": [{"@": "PrettyPFA line 14", "string": "mu"}]}, {"@": "PrettyPFA line 14", "cell": "baseline", "path": [{"@": "PrettyPFA line 14", "string": "sig"}]}]}}}, {"@": "PrettyPFA line 15", "let": {"altLL": {"@": "PrettyPFA line 15", "prob.dist.gaussianLL": ["input", {"@": "PrettyPFA line 15", "cell": "alternate", "path": [{"@": "PrettyPFA line 15", "string": "mu"}]}, {"@": "PrettyPFA line 15", "cell": "alternate", "path": [{"@": "PrettyPFA line 15", "string": "sig"}]}]}}}, {"@": "PrettyPFA lines 16-17", "cell": "cusum", "to": {"params": [{"old": "double"}], "ret": "double", "do": [{"@": "PrettyPFA line 17", "stat.change.updateCUSUM": [{"@": "PrettyPFA line 17", "-": ["altLL", "baseLL"]}, "old", {"@": "PrettyPFA line 17", "cell": "reset"}]}]}}], "cells": {"cusum": {"@": "PrettyPFA line 8", "type": "double", "init": 0.0, "shared": true, "rollback": false}, "reset": {"@": "PrettyPFA line 9", "type": "double", "init": 0.0, "shared": false, "rollback": false}, "alternate": {"@": "PrettyPFA line 11", "type": {"fields": [{"type": "double", "name": "mu"}, {"type": "double", "name": "sig"}], "type": "record", "name": "Record_1"}, "init": {"mu": 24.23076923076923, "sig": 9.420276925706693}, "shared": false, "rollback": false}, "baseline": {"@": "PrettyPFA line 10", "type": "Record_1", "init": {"mu": 10.448598130841122, "sig": 2.0227690446443916}, "shared": false, "rollback": false}}}

examples/edge_intelligence/trained_models/tensorflow_model/checkpoint

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+model_checkpoint_path: "model.ckpt-2000"
+all_model_checkpoint_paths: "model.ckpt-1"
+all_model_checkpoint_paths: "model.ckpt-2000"