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1. 定义数据结构
2. 读取训练数据
3. 选择向量
4. 训练模型
5. 预测
实现：
using System;
using System.Collections.Generic;
using System.Configuration;
using System.Text;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
namespace MLNetLab
{
// IrisData is used to provide training data, and as
// input for prediction operations
// - First 4 properties are inputs/features used to predict the label
// - Label is what you are predicting, and is only set when training
public class IrisData
{
[Column("0")]
public float SepalLength;
[Column("1")]
public float SepalWidth;
[Column("2")]
public float PetalLength;
[Column("3")]
public float PetalWidth;
[Column("4")]
[ColumnName("Label")]
public string Label;
}
// IrisPrediction is the result returned from prediction operations
public class IrisPrediction
{
[ColumnName("PredictedLabel")]
public string PredictedLabels;
}
public class IrisRunner
{
private static string dataPath = ConfigurationManager.AppSettings["iris_file_name"];
public static void Go()
{
// STEP 2: Create a pipeline and load your data
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(dataPath).CreateFrom<IrisData>(separator: ','));
// STEP 3: Transform your data
// Assign numeric values to text in the "Label" column, because only
// numbers can be processed during model training
pipeline.Add(new Dictionarizer("Label"));
// Puts all features into a vector
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
// STEP 4: Add learner
// Add a learning algorithm to the pipeline.
// This is a classification scenario (What type of iris is this?)
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
// Convert the Label back into original text (after converting to number in step 3)
pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" });
// STEP 5: Train your model based on the data set
var model = pipeline.Train<IrisData, IrisPrediction>();
// STEP 6: Use your model to make a prediction
// You can change these numbers to test different predictions
var prediction = model.Predict(new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
});
Console.WriteLine($"Predicted flower type is: {prediction.PredictedLabels}");
}
}
}
调用：
static void Main(string[] args)
{
SpeechSynthesizer synthesizer = new SpeechSynthesizer();
synthesizer.Volume = 100; // 0...100
synthesizer.Rate = -3; // -10...10
// Synchronous
synthesizer.Speak("Hello , Microsoft");
// Asynchronous
//synthesizer.SpeakAsync("Hello World");
Console.ReadLine();
}