CSCI-4830-002-2014 · thefyk · Nov 9, 2014 · Nov 9, 2014 · Nov 9, 2014 · Nov 10, 2014
diff --git a/1CD3.png b/1CD3.png
diff --git a/1CMongo.png b/1CMongo.png
diff --git a/1Neo4j.png b/1Neo4j.png
diff --git a/1bCMongo.png b/1bCMongo.png
diff --git a/1cCMongo.png b/1cCMongo.png
diff --git a/2AMongo.png b/2AMongo.png
diff --git a/2BMongo.png b/2BMongo.png
diff --git a/2CD3.png b/2CD3.png
diff --git a/2CMongo.png b/2CMongo.png
diff --git a/2Neo4j.png b/2Neo4j.png
diff --git a/3CD3.png b/3CD3.png
diff --git a/3Neo4j.png b/3Neo4j.png
diff --git a/4CD3.png b/4CD3.png
diff --git a/C5D3.png b/C5D3.png
diff --git a/C6D3.png b/C6D3.png
diff --git a/C7D3.png b/C7D3.png
diff --git a/README.md b/README.md
@@ -1,40 +1,40 @@
 # Name
 
-write-your-name
+Michael Fyk
 
 # How many points have you earned?
 
-0/100
+90/100
 
 (Make your own calculation and replace the number 0 with the points you think you've earned.)
 
 # How many hours have you spent on this?
 
-fill-in-your-answer
+5
 
 # When did you first start working on this week's learning challenges?
 
-fill-in-your-answer
+Saturday
 
 # What is the most difficult part about this week's challenge?
 
-fill-in-your-answer
+Finding access to Matlab, formatting the MongoDB data
 
 # Graph Database I
 
 ## Checkpoints (5 points x 3 = 15 points)
 
 ### 1. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](1Neo4j.png)
 
 ### 2. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](2Neo4j.png)
 
 ### 3. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](3Neo4j.png)
 
 # MongoDB IV
 
@@ -44,32 +44,32 @@ fill-in-your-answer
 
 Count
 
-![screenshot](screenshot.png)
+![screenshot](1CMongo.png)
 
 FindOne
 
-![screenshot](screenshot.png)
+![screenshot](1bCMongo.png)
 
 
 Query
 
-![screenshot](screenshot.png)
+![screenshot](1cCMongo.png)
 
 
 ### 2. (5 point)
 
 Count
 
-![screenshot](screenshot.png)
+![screenshot](2AMongo.png)
 
 FindOne
 
-![screenshot](screenshot.png)
+![screenshot](2BMongo.png)
 
 
 Query
 
-![screenshot](screenshot.png)
+![screenshot](2CMongo.png)
 
 ### 3. (5 point)
 
@@ -112,141 +112,141 @@ Query 2
 
 ### 1. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](http://i.imgur.com/oI6SWSC.png)
 
 ### 2. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](http://i.imgur.com/8xMsYhr.png)
 
 ## Challenges (5 points x 4 = 20 points)
 
 ### 1. (5 point)
 
-Accuracy = {fill-in-your-best-number}
+Accuracy = 81.5815%
 
 Features/Parameters
 
 	%%%%%%%%% Choose feature source %%%%%%%%%%%
-	add_accelerometer = {TODO}; % Adds accelerometer features
-	add_gyroscope = {TODO}; % Adds gyroscope features
-	add_magneticField = {TODO}; % Adds magnetic field features
-	add_gravity = {TODO}; % Adds gravity features
-	add_linearAcceleration = {TODO}; % Adds linear acceleration features
-	add_orientation = {TODO}; % Adds azimuth, pich and roll features
-	add_light = {TODO}; % Adds light value
-	add_proximity = {TODO}; % Adds proximity value
-	add_studentID = {TODO}; % Adds student ID
+add_accelerometer = true; % Adds accelerometer features
+add_gyroscope = true; % Adds gyroscope features
+add_magneticField = true; % Adds magnetic field features
+add_gravity = false; % Adds gravity features
+add_linearAcceleration = true; % Adds linear acceleration features
+add_orientation = false; % Adds azimuth, pich and roll features
+add_light = false; % Adds light value
+add_proximity = true; % Adds proximity value
+add_studentID = false; % Adds student ID
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 	%%%%%%%%% Choose machine learning classifier parameters %%%%%%%%%%%%%%%
-	numTrees = {TODO}; % Try different number of trees for the Random Forest classifier
-	sigma = {TODO}; % Try different values of sigma for the Support Vector Machine classifier
-	dist = {TODO}; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
-	K = {TODO}; % Try different values of K for the K-nearest Neighbor classifier
+	numTrees = 1; % Try different number of trees for the Random Forest classifier
+	sigma = 1; % Try different values of sigma for the Support Vector Machine classifier
+	dist = 'kernel; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
+	K = 16; % Try different values of K for the K-nearest Neighbor classifier
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 ### 2. (5 point)
 
-Accuracy = {fill-in-your-best-number}
+Accuracy = 75.3134%
 
 Features/Parameters
 
 	%%%%%%%%% Choose feature source %%%%%%%%%%%
-	add_accelerometer = {TODO}; % Adds accelerometer features
-	add_gyroscope = {TODO}; % Adds gyroscope features
-	add_magneticField = {TODO}; % Adds magnetic field features
-	add_gravity = {TODO}; % Adds gravity features
-	add_linearAcceleration = {TODO}; % Adds linear acceleration features
-	add_orientation = {TODO}; % Adds azimuth, pich and roll features
-	add_light = {TODO}; % Adds light value
-	add_proximity = {TODO}; % Adds proximity value
-	add_studentID = {TODO}; % Adds student ID
+add_accelerometer = true; % Adds accelerometer features
+add_gyroscope = true; % Adds gyroscope features
+add_magneticField = true; % Adds magnetic field features
+add_gravity = false; % Adds gravity features
+add_linearAcceleration = false; % Adds linear acceleration features
+add_orientation = false; % Adds azimuth, pich and roll features
+add_light = false; % Adds light value
+add_proximity = true; % Adds proximity value
+add_studentID = false; % Adds student ID
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 	%%%%%%%%% Choose machine learning classifier parameters %%%%%%%%%%%%%%%
-	numTrees = {TODO}; % Try different number of trees for the Random Forest classifier
-	sigma = {TODO}; % Try different values of sigma for the Support Vector Machine classifier
-	dist = {TODO}; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
-	K = {TODO}; % Try different values of K for the K-nearest Neighbor classifier
+numTrees = 1; % Try different number of trees for the Random Forest classifier
+sigma = 1; % Try different values of sigma for the Support Vector Machine classifier
+dist = 'kernel'; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
+K = 16; % Try different values of K for the K-nearest Neighbor classifier
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 ### 3. (5 point)
 
-Accuracy = {fill-in-your-best-number}
+Accuracy = 94.1176%
 
 Features/Parameters
 
 	%%%%%%%%% Choose feature source %%%%%%%%%%%
-	add_accelerometer = {TODO}; % Adds accelerometer features
-	add_gyroscope = {TODO}; % Adds gyroscope features
-	add_magneticField = {TODO}; % Adds magnetic field features
-	add_gravity = {TODO}; % Adds gravity features
-	add_linearAcceleration = {TODO}; % Adds linear acceleration features
-	add_orientation = {TODO}; % Adds azimuth, pich and roll features
-	add_light = {TODO}; % Adds light value
-	add_proximity = {TODO}; % Adds proximity value
-	add_studentID = {TODO}; % Adds student ID
+	add_accelerometer = true; % Adds accelerometer features
+add_gyroscope = true; % Adds gyroscope features
+add_magneticField = true; % Adds magnetic field features
+add_gravity = true; % Adds gravity features
+add_linearAcceleration = true; % Adds linear acceleration features
+add_orientation = false; % Adds azimuth, pich and roll features
+add_light = false; % Adds light value
+add_proximity = true; % Adds proximity value
+add_studentID = false; % Adds student ID
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 	%%%%%%%%% Choose machine learning classifier parameters %%%%%%%%%%%%%%%
-	numTrees = {TODO}; % Try different number of trees for the Random Forest classifier
-	sigma = {TODO}; % Try different values of sigma for the Support Vector Machine classifier
-	dist = {TODO}; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
-	K = {TODO}; % Try different values of K for the K-nearest Neighbor classifier
+	numTrees = 1; % Try different number of trees for the Random Forest classifier
+sigma = 3; % Try different values of sigma for the Support Vector Machine classifier
+dist = 'kernel'; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
+K = 16; % Try different values of K for the K-nearest Neighbor classifier
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 ### 4. (5 point)
 
-Accuracy = {fill-in-your-best-number}
+Accuracy = 89.2960%
 
 Features/Parameters
 
 	%%%%%%%%% Choose feature source %%%%%%%%%%%
-	add_accelerometer = {TODO}; % Adds accelerometer features
-	add_gyroscope = {TODO}; % Adds gyroscope features
-	add_magneticField = {TODO}; % Adds magnetic field features
-	add_gravity = {TODO}; % Adds gravity features
-	add_linearAcceleration = {TODO}; % Adds linear acceleration features
-	add_orientation = {TODO}; % Adds azimuth, pich and roll features
-	add_light = {TODO}; % Adds light value
-	add_proximity = {TODO}; % Adds proximity value
-	add_studentID = {TODO}; % Adds student ID
+add_accelerometer = true; % Adds accelerometer features
+add_gyroscope = true; % Adds gyroscope features
+add_magneticField = true; % Adds magnetic field features
+add_gravity = true; % Adds gravity features
+add_linearAcceleration = true; % Adds linear acceleration features
+add_orientation = false; % Adds azimuth, pich and roll features
+add_light = false; % Adds light value
+add_proximity = true; % Adds proximity value
+add_studentID = false; % Adds student ID
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 	%%%%%%%%% Choose machine learning classifier parameters %%%%%%%%%%%%%%%
-	numTrees = {TODO}; % Try different number of trees for the Random Forest classifier
-	sigma = {TODO}; % Try different values of sigma for the Support Vector Machine classifier
-	dist = {TODO}; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
-	K = {TODO}; % Try different values of K for the K-nearest Neighbor classifier
+numTrees = 50; % Try different number of trees for the Random Forest classifier
+sigma = 3; % Try different values of sigma for the Support Vector Machine classifier
+dist = 'kernel'; % Try different distributions = {'normal', 'kernel', 'mvmn' , 'mn'} for Naive Bayes classifier
+K = 16; % Try different values of K for the K-nearest Neighbor classifier
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
 # D3 (VI) (5 points x 7 = 35 points)
 
 ### 1. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](1CD3.png)
 
 ### 2. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](2CD3.png)
 
 ### 3. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](3CD3.png)
 
 ### 4. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](4CD3.png)
 
 ### 5. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](C5D3.png)
 
 ### 6. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](C6D3.png)
 
 ### 7. (5 point)
 
-![screenshot](screenshot.png)
+![screenshot](C7D3.png)
diff --git a/app.js b/app.js
@@ -3,7 +3,7 @@ var express = require('express');
 var app = express();
 
 var mongo = require('mongoskin');
-var db = mongo.db("mongodb://{USERNAME}:{PASSWORD}@{SEVERURL}", {native_parser:true});
+var db = mongo.db("mongodb://user and [email protected]:51980/yelpdata", {native_parser:true});
 
 app.engine('.html', require('ejs').__express);
 app.set('views', __dirname);
@@ -14,7 +14,8 @@ app.get('/state/:state', function(req, res) {
     var projection = {};
     db.collection('business')
         .find(query,projection)
-        .limit(20)
+        .sort( { review_count: -1 } )
+        .limit(200)
         .toArray(function (err, items) {        
             res.render("business_map", {data: items});        
     });

diff --git a/business.mongo.json b/business.mongo.json
diff --git a/business_map.html b/business_map.html
@@ -38,9 +38,9 @@
       }
 
       .markers circle {
-        fill: brown;
         stroke: black;
         stroke-width: 1.5px;
+        fill-opacity: .5;
       }
 
     </style>      
@@ -57,8 +57,15 @@
 
     <script type="text/javascript">
 
+
+
       var dataset = <%- JSON.stringify(data) %>;   
 
+      var rScale = d3.scale.linear()
+            .domain([50, 5000])
+            .range(3, 15);
+
+
       // Set the center location (lat/lng) to be the location of the first business                  
       var first = dataset[0];
       var centerLatLng = new google.maps.LatLng(first.latitude, first.longitude);
@@ -94,17 +101,23 @@
 
         // Add a circle.
         marker.append("svg:circle")
-            .attr("r", 4.5)
+            .attr("r", function(d) {return d.review_count/5000*15;})
             .attr("cx", padding)
             .attr("cy", padding)
+            .style("fill", function(d) {
+            if (d.stars >= 4) {return "red"}
+            else if (d.stars >=2) {return "brown"} else
 
+              { return "black" }
+        ;})                                    
         // Add a label.
-        marker.append("svg:text")
+
+        /*marker.append("svg:text")
             .attr("x", padding + 7)
             .attr("y", padding)
             .attr("dy", ".31em")
-            .text(function(d) { return d.name; });
-
+            .text(function(d) { return d.review_count; });
+*/
         function transform(d) {
           d = new google.maps.LatLng(d.latitude, d.longitude);
           d = projection.fromLatLngToDivPixel(d);

diff --git a/node_modules/ejs/.gitmodules b/node_modules/ejs/.gitmodules
diff --git a/node_modules/ejs/.npmignore b/node_modules/ejs/.npmignore
diff --git a/node_modules/ejs/.travis.yml b/node_modules/ejs/.travis.yml