static void test_2()
{
Dictionary <string, object> all_flaws = new Dictionary <string, object>();
string root = @"C:\Tools\avia\ClassifyLog"; // @"C:\Tools\avia\ClassifyLog";
standard spec = standard.LoadSpec(@"C:\Tools\avia\classify.xml");
Dictionary <string, object> specs = spec.ToDictionary();
foreach (string fn in System.IO.Directory.GetFiles(root, "*.txt"))
{
flaw df = new flaw(fn);
foreach (Dictionary <string, string> f in df.Flaws)
{
string surface = f["surface"];
string sort = f["sort"];
string k = $"{sort}_{surface}";
if (!all_flaws.ContainsKey(k))
{
all_flaws.Add(k, new List <Dictionary <string, object> >());
}
List <Dictionary <string, object> > kv = (List <Dictionary <string, object> >)all_flaws[k];
kv.Add(f.ToDictionary(pair => pair.Key, pair => (object)pair.Value));
}
}
// save json
{
var jss = new System.Web.Script.Serialization.JavaScriptSerializer();
string s = jss.Serialize(all_flaws);
System.IO.File.WriteAllText("all_flaws.json", s);
}
}
public static void predict_main(System.Collections.Specialized.StringDictionary args)
{
flaw f = new flaw(args["input"]);
string root = System.IO.Path.GetDirectoryName(System.Reflection.Assembly.GetEntryAssembly().Location);
System.IO.Directory.CreateDirectory(System.IO.Path.Combine(root, "tmp"));
try
{
string[] keys = load_all_keys();
Dictionary <string, object> d = new Dictionary <string, object>();
foreach (string k in keys)
{
if (f.Counts.ContainsKey(k))
{
d.Add(k, f.Counts[k]);
}
else
{
d.Add(k, 0);
}
}
var jss = new System.Web.Script.Serialization.JavaScriptSerializer();
//ret = jss.Deserialize<List<Dictionary<string, object>>>(str);
string str = jss.Serialize(d);
System.IO.File.WriteAllText(System.IO.Path.Combine(root, "tmp", "test.json"), str);
int ret;
string[] lines = runExe("dotnet.exe", $@"mlApp1.dll predict --input {System.IO.Path.Combine(root, "tmp", "test.json")}", out ret, systemapp: true,
workdir: $@"{System.IO.Path.Combine(root, "publish")}");
System.Console.WriteLine($"{string.Join(System.Environment.NewLine, lines)}");
}
catch (Exception) { }
}
static Dictionary <string, object>[] prep(string folder, System.Collections.Specialized.StringDictionary[] vdata)
{
List <Dictionary <string, object> > ret = new List <Dictionary <string, object> >();
System.Text.RegularExpressions.Regex r = new Regex(@"classify-(\d{4}).txt");
string root = folder;
foreach (string fn in System.IO.Directory.GetFiles(root))
{
Match m = r.Match(fn);
if (m.Success && m.Groups.Count > 1)
{
Dictionary <string, object> report = new Dictionary <string, object>();
StringDictionary vd = find_device(vdata, m.Groups[1].Value);
System.Console.WriteLine("=======================================");
Program.logIt($"Prep device data: imei={vd?["imei"]}, model={vd?["Model"]}, color={vd?["Color"]}");
Program.logIt($"Load device flaws from: {fn}");
flaw f = new flaw(fn);
string sdump = f.dump();
// save data in report
report.Add("imei", vd?["imei"]);
report.Add("model", vd?["Model"]);
report.Add("color", vd?["Color"]);
report.Add("XPO", vd?["XPO"]);
report.Add("VZW", vd?["VZW"]);
report.Add("OE", f.Grade);
report.Add("dump", sdump);
#if false
{
string[] keys = GradeChecker.Properties.Resources.grade_keys.Split(new string[] { Environment.NewLine }, StringSplitOptions.RemoveEmptyEntries);
foreach (string k in keys)
{
int c = 0;
if (f.Counts.ContainsKey(k))
{
c = f.Counts[k];
}
report.Add(k, c.ToString());
}
}
#else
foreach (KeyValuePair <string, Tuple <int, int> > kvp in f.Counts)
{
report.Add(kvp.Key, kvp.Value.Item1);
}
#endif
ret.Add(report);
}
}
// save
//try
//{
// var jss = new System.Web.Script.Serialization.JavaScriptSerializer();
// string s = jss.Serialize(ret);
// System.IO.File.WriteAllText("samples.json", s);
//}
//catch (Exception) { }
return(ret.ToArray());
}
static Dictionary <string, Tuple <int, int> >[] load_counts_from_folder(string folder)
{
//string folder = @"C:\Tools\avia\ClassifyLog";
List <Dictionary <string, Tuple <int, int> > > db = new List <Dictionary <string, Tuple <int, int> > >();
foreach (string fn in System.IO.Directory.GetFiles(folder, "*.txt"))
{
//flaw f = new flaw(@"C:\projects\avia\logfiles\classify-0083.txt");
flaw f = new flaw(fn);
f.dump();
//f.recount();
// _counts to json
// get last-4-digit
string s = System.IO.Path.GetFileNameWithoutExtension(fn);
s = s.Substring(s.Length - 4);
f.Counts.Add("id", new Tuple <int, int>(Int32.Parse(s), Array.IndexOf(gradeing_label, f.Grade)));
db.Add(f.Counts);
}
return(db.ToArray());
}
static void test()
{
string fn = @"C:\Tools\avia\ClassifyLog\classify-8738.txt";
flaw f = new flaw(fn);
try
{
string[] keys = load_all_keys();
Dictionary <string, object> d = new Dictionary <string, object>();
foreach (string k in keys)
{
if (f.Counts.ContainsKey(k))
{
d.Add(k, f.Counts[k]);
}
else
{
d.Add(k, 0);
}
}
var jss = new System.Web.Script.Serialization.JavaScriptSerializer();
//ret = jss.Deserialize<List<Dictionary<string, object>>>(str);
string str = jss.Serialize(d);
System.IO.File.WriteAllText(@"C:\Tools\avia\tmp\test.json", str);
int ret;
string[] lines = runExe("dotnet.exe", @"mlApp1.dll predict --input C:\Tools\avia\tmp\test.json", out ret, systemapp: true, workdir: @"C:\Users\qa\source\repos\mlApp1\mlApp1\bin\Debug\netcoreapp2.2\publish");
// prepare data
//ModelInput mi = new ModelInput();
//foreach (PropertyInfo pi in mi.GetType().GetProperties())
//{
// string name = "";
// foreach(var p in pi.CustomAttributes)
// {
// if (string.Compare(p.AttributeType.Name, "ColumnNameAttribute") == 0)
// {
// name = p.ConstructorArguments[0].Value?.ToString();
// break;
// }
// }
// if (string.Compare(name, "VZW")==0)
// {
// pi.SetValue(mi, Array.IndexOf(gradeing_label, "D"));
// }
// else
// {
// name = name.Replace('_', '-');
// if (d.ContainsKey(name))
// {
// pi.SetValue(mi, d[name]);
// }
// else
// pi.SetValue(mi, 0);
// }
//}
// load model
//MLContext mlContext = new MLContext();
//DataViewSchema modelInputSchema;
//ITransformer mlModel = mlContext.Model.Load(@"C:\Users\qa\source\repos\mlApp1\mlApp1ML.Model\MLModel.zip", out modelInputSchema);
//var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
//ModelOutput result = predEngine.Predict(mi);
}
catch (Exception) { }
}
