readme changes

Author: mediumTaj

CHANGELOG.md

@@ -5,7 +5,7 @@ Change Log
 _2016-05-28_
 
  * New: Added Tone Analyzer v3 abstraction
- * New: Added Tradeoff Analyzer abstraction
+ * New: Added Tradeoff Analytics abstraction
  * New: Added Conversation abstraction
  * New: Added Visual Recognition v3 abstraction
  * Fix: Creating test project dynamically for Travis CL integration

README.md

@@ -14,6 +14,10 @@ Use this SDK to build Watson-powered applications in Unity. It comes with a set
   * [Language Translation](#language-translation)
   * [Dialog](#dialog)
   * [Natural Language Classifier](#natural-language-classifier)
+  * [Tone Analyzer](#tone-analyzer)
+  * [Tradeoff Analytics](#tradeoff-analytics)
+  * [Conversation](#conversation)
+  * [Visual Recognition](#visual-recognition)
 * [Developing a basic application in one minute](#developing-a-basic-application-in-one-minute)
 * [Documentation](#documentation)
 * [License](#license)
@@ -210,9 +214,121 @@ You can use the Natural Language Classifier Editor to import and export classifi
 2. In the **Name** field, specify a name for the classifier.
 3. Click **Create**.
 
-### Conversation
-<!-- conversation description here: Change link below -->
+### Tone Analyzer
+The [Tone Analyzer][tone_analyzer] service detects emotions, social tendencies and writing style from text input.
 
+```
+ToneAnalyzer m_ToneAnalyzer = new ToneAnalyzer();
+    string m_StringToTestTone = "This service enables people to discover and understand, and revise the impact of tone in their content. It uses linguistic analysis to detect and interpret emotional, social, and language cues found in text.";
+
+	void Start () {
+        m_ToneAnalyzer.GetToneAnalyze( OnGetToneAnalyze, m_StringToTestTone, "TEST");
+	}
+
+    private void OnGetToneAnalyze( ToneAnalyzerResponse resp , string data)
+    {
+        Debug.Log("Response: " +resp + " - " + data);
+    }
+```
+
+### Tradeoff Analytics
+The [Tradeoff Analytics][tradeoff_analytics] service helps people make better decisions when faced with multiple, sometimes conflicting, goals and alternatives.
+
+```
+void Start () {
+        Problem problemToSolve = new Problem();
+        problemToSolve.subject = "Test Subject";
+
+        List<Column> listColumn = new List<Column>();
+        Column columnPrice = new Column();
+        columnPrice.description = "Price Column to minimize";
+        columnPrice.range = new ValueRange();
+        ((ValueRange)columnPrice.range).high = 600;
+        ((ValueRange)columnPrice.range).low = 0;
+        columnPrice.type = "numeric";
+        columnPrice.key = "price";
+        columnPrice.full_name = "Price";
+        columnPrice.goal = "min";
+        columnPrice.is_objective = true;
+        columnPrice.format = "$####0.00";
+
+        Column columnWeight = new Column();
+        columnWeight.description = "Weight Column to minimize";
+        columnWeight.type = "numeric";
+        columnWeight.key = "weight";
+        columnWeight.full_name = "Weight";
+        columnWeight.goal = "min";
+        columnWeight.is_objective = true;
+        columnWeight.format = "####0 g";
+
+        Column columnBrandName = new Column();
+        columnBrandName.description = "All Brand Names";
+        columnBrandName.type = "categorical";
+        columnBrandName.key = "brand";
+        columnBrandName.full_name = "Brand";
+        columnBrandName.goal = "max";
+        columnBrandName.is_objective = true;
+        columnBrandName.preference = new string[]{"Samsung", "Apple", "HTC"};
+        columnBrandName.range = new CategoricalRange();
+        ((CategoricalRange)columnBrandName.range).keys = new string[]{"Samsung", "Apple", "HTC"};
+
+        listColumn.Add(columnPrice);
+        listColumn.Add(columnWeight);
+
+        problemToSolve.columns = listColumn.ToArray();
+
+
+        List<Option> listOption = new List<Option>();
+
+        Option option1 = new Option();
+        option1.key = "1";
+        option1.name = "Samsung Galaxy S4";
+        option1.values = new TestDataValue();
+        (option1.values as TestDataValue).weight = 130;
+        (option1.values as TestDataValue).brand = "Samsung";
+        (option1.values as TestDataValue).price = 249;
+        listOption.Add(option1);
+
+        Option option2 = new Option();
+        option2.key = "2";
+        option2.name = "Apple iPhone 5";
+        option2.values = new TestDataValue();
+        (option2.values as TestDataValue).weight = 112;
+        (option2.values as TestDataValue).brand = "Apple";
+        (option2.values as TestDataValue).price = 599;
+        listOption.Add(option2);
+
+        Option option3 = new Option();
+        option3.key = "3";
+        option3.name = "HTC One";
+        option3.values = new TestDataValue();
+        (option3.values as TestDataValue).weight = 143;
+        (option3.values as TestDataValue).brand = "HTC";
+        (option3.values as TestDataValue).price = 299;
+        listOption.Add(option3);
+
+        problemToSolve.options = listOption.ToArray();
+
+        m_TradeoffAnalytics.GetDilemma( OnGetDilemma, problemToSolve, false );
+	}
+
+    private void OnGetDilemma( DilemmasResponse resp )
+    {
+        Debug.Log("Response: " + resp);
+    }
+
+    /// <summary>
+    /// Application data value.
+    /// </summary>
+    public class TestDataValue : IBM.Watson.DeveloperCloud.Services.TradeoffAnalytics.v1.ApplicationDataValue
+    {
+        public double price { get; set; }
+        public double weight { get; set; }
+        public string brand { get; set; }
+    }
+```
+
+<!--### Conversation
 ```cs
 private Conversation m_Conversation = new Conversation();
 private string m_WorkspaceID = "car_demo_1";
@@ -230,7 +346,26 @@ void OnMessage (DataModels.MessageResponse resp)
 
 	Debug.Log("response: " + resp.output.text);
 }
+```-->
+
+<!--### Visual Recognition
+The [Visual Recognition][visual_recognition] service uses deep learning algorithms to analyze images for scenes, objects, faces, text and other content and returns keywords about that content. You can also train custom classifiers to classify images.
+
+##### Classifying an image
+```
+```
+
+##### Creating new classifiers
+```
+```
+
+##### Detecting faces
+```
+```
+
+##### Recognizing text
 ```
+```-->
 
 ## Developing a basic application in one minute
 You can quickly develop a basic application that uses the Speech to Text service and the Natural Language Classifier service by using the prefabs that come with the SDK. Ensure that you prepare the test data before you complete the the following steps:
@@ -280,3 +415,5 @@ See [CONTRIBUTING.md](.github/CONTRIBUTING.md).
 [sentiment_analysis]: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis
 [tone_analyzer]: http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/tone-analyzer/
 [tradeoff_analytics]: http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/tradeoff-analytics/
+[conversation]:http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/dialog/api/v1/
+[visual_recognition]:http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/visual-recognition/