コード例 #1
0
        public static void Main(string[] args)
        {
            ToolArguments parsedArgs = new ToolArguments(args, "None", new MainClass());

            PorterStemmer stemmer = new PorterStemmer();

            if (parsedArgs["stem"] != null)
                Console.WriteLine(parsedArgs["stem"] + " => " + stemmer.stemTerm(parsedArgs["stem"]));

            if (parsedArgs["freqrows"] != null) {
                DataReader reader = new DataReader(parsedArgs["f"]);
                for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
                    TwoTuple<int, int> counts = FrequencyTools.WordCount(parsedArgs["freqrows"], row[1]);
                    Console.WriteLine(counts.one + "," + counts.two + ",\"" + row[2] + "\"");
                }
            }

            if (parsedArgs["emotion"] != null) {
                ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
                double[] emotions = sensor.EstimateEmotions(parsedArgs["emotion"]);
                for (int ii = 0; ii < (int) ANEWEmotionSensor.Emotions.COUNT; ii++)
                    Console.WriteLine(((ANEWEmotionSensor.Emotions) ii).ToString() + ": " + emotions[ii]);
            }

            if (parsedArgs["emorows"] != null) {
                int rows = 0, valids = 0;
                ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
                DataReader reader = new DataReader(parsedArgs["f"]);
                for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
                    rows++;
                    double[] emotions = sensor.EstimateEmotions(row[1]);
                    Console.WriteLine("\"" + row[0] + "\"," + emotions[0] + "," + emotions[1] + "," + emotions[2] + "," + emotions[3] + "," + emotions[4] + "," + emotions[5] + "," + emotions[6] + "," + emotions[7] + ",\"" + row[2] + "\"");
                    if (!double.IsNaN(emotions[0]))
                        valids++;
                }
            }
        }
コード例 #2
0
ファイル: Main.cs プロジェクト: killix/Virsona-ChatBot-Tools
        public static void Main(string[] args)
        {
            ToolArguments parsedArgs = new ToolArguments(args, "None", new MainClass());

            PorterStemmer stemmer = new PorterStemmer();

            if (parsedArgs["stem"] != null)
                Console.WriteLine(parsedArgs["stem"] + " => " + stemmer.stemTerm(parsedArgs["stem"]));

            /*ANEWEmotionSensor sensor2 = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
            for (int rr = 0; rr < sensor2.positiveMatrix.GetLength(0); rr++) {
                for (int cc = 0; cc < sensor2.positiveMatrix.GetLength(1); cc++)
                    Console.Write(sensor2.positiveMatrix[rr, cc] + ", ");
                Console.WriteLine(" - ");
            }
            for (int rr = 0; rr < sensor2.negativeMatrix.GetLength(0); rr++) {
                for (int cc = 0; cc < sensor2.negativeMatrix.GetLength(1); cc++)
                    Console.Write(sensor2.negativeMatrix[rr, cc] + ", ");
                Console.WriteLine(" - ");
            }
            return;*/

            if (parsedArgs["freqrows"] != null) {
                DataReader reader = new DataReader(parsedArgs["f"]);
                for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
                    TwoTuple<int, int> counts = FrequencyTools.WordCount(parsedArgs["freqrows"], row[1]);
                    Console.WriteLine(counts.one + "," + counts.two + ",\"" + row[2] + "\"");
                }
            }

            if (parsedArgs["emotion"] != null) {
                ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
                double[] emotions = sensor.EstimateEmotions(parsedArgs["emotion"]);
                for (int ii = 0; ii < (int) ANEWEmotionSensor.Emotions.COUNT; ii++)
                    Console.WriteLine(((ANEWEmotionSensor.Emotions) ii).ToString() + ": " + emotions[ii]);
            }

            if (parsedArgs["emorows"] != null) {
                int rows = 0, valids = 0;
                ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
                DataReader reader = new DataReader(parsedArgs["f"]);
                for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
                    rows++;
                    double[] emotions = sensor.EstimateEmotions(row[1]);
                    Console.WriteLine("\"" + row[0] + "\"," + emotions[0] + "," + emotions[1] + "," + emotions[2] + "," + emotions[3] + "," + emotions[4] + "," + emotions[5] + "," + emotions[6] + "," + emotions[7] + ",\"" + row[2] + "\"");
                    if (!double.IsNaN(emotions[0]))
                        valids++;
                }
            }

            if (parsedArgs["eimpute"] != null) {
                ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");

                // DIAGNOSTIC
                /*List<List<string>> rows = new List<List<string>>();
                rows.Add(TwitterUtilities.SplitWords("happy aaaa cccc"));
                rows.Add(TwitterUtilities.SplitWords("sad bbbb cccc"));

                IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> inputed = sensor.ImputeEmotionalContent(rows, 1000);
                foreach (KeyValuePair<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> kvp in inputed)
                    Console.WriteLine(kvp.Key + ": " + kvp.Value.one.Mean + ", " + kvp.Value.two.Mean + ", " + kvp.Value.three.Mean);*/

                bool smallFile = false;
                if (smallFile) {
                    DataReader reader = new DataReader(parsedArgs["f"]);
                    List<List<string>> rows = new List<List<string>>();
                    for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
                        Console.WriteLine(row);
                        rows.Add(TwitterUtilities.SplitWords(row[10].ToLower()));
                    }
                    reader.Close();

                    /*IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> inputed = sensor.ImputeEmotionalContent(rows, 10);
                    double minv = 1, maxv = 0;
                    foreach (KeyValuePair<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> kvp in inputed) {
                        minv = Math.Min(minv, kvp.Value.one.Mean);
                        maxv = Math.Max(maxv, kvp.Value.one.Mean);
                        Console.WriteLine(kvp.Key + ": " + kvp.Value.one.Mean + " x " + kvp.Value.one.Variance + ", " + kvp.Value.two.Mean + ", " + kvp.Value.three.Mean);
                    }

                    Console.WriteLine("Min: " + minv + ", Max: " + maxv);*/

                    sensor.ImputeEmotionalContent(rows, 10, parsedArgs["f"] + "imputed");
                } else {
                    sensor.ImputeEmotionalContentFromFile(parsedArgs["f"], 11, 0, parsedArgs["f"].Substring(0, parsedArgs["f"].Length - 4) + "imputed.csv");
                }

                uint jj = 0;
                using (var stream = File.CreateText(parsedArgs["f"] + "result")) {
                    jj++;
                    if (jj % 1000 == 0)
                        Console.WriteLine("#" + jj);

                    DataReader reader = new DataReader(parsedArgs["f"]);
                    for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
                        double[] emotions = sensor.EstimateEmotions(row[11]);
                        for (int ii = 0; ii < 11; ii++)
                            stream.Write(row[ii] + ",");
                        stream.WriteLine(emotions[0] + "," + emotions[1] + "," + emotions[2] + "," + emotions[3] + "," + emotions[4] + "," + emotions[5] + "," + emotions[6] + "," + emotions[7]);
                    }
                }
            }
        }