public void DataFrameDictionariesRoundtrip()
{
DataFrame frame = new DataFrame(
new DataHeader <string>("name"),
new DataHeader <double>("height"),
new DataHeader <bool?>("male")
);
frame.AddRow("a", 5.0, false);
frame.AddRow("b", 6.0, true);
frame.AddRow("c", 5.5, null);
List <Dictionary <string, object> > dictionaries = frame.ToDictionaries().ToList();
Assert.IsTrue(dictionaries.Count == frame.Rows.Count);
Assert.IsTrue(dictionaries[0].Count == frame.Columns.Count);
DataFrame frame2 = DataFrame.FromDictionaries(dictionaries);
Assert.IsTrue(frame2.Rows.Count == frame.Rows.Count);
Assert.IsTrue(frame2.Columns.Count == frame.Columns.Count);
Assert.IsTrue(frame2.Columns[0].Name == frame.Columns[0].Name);
Assert.IsTrue(frame2.Columns[1].StorageType == frame.Columns[1].StorageType);
Assert.IsTrue(frame2.Rows[2]["male"] == frame2.Rows[2]["male"]);
}
public static void Main(string[] args)
{
DataFrame df = new DataFrame();
df.AddColumn("name", typeof(string));
df.AddColumn("age", typeof(int));
df.AddRow("John", 27);
df.AddRow("Michael", 98);
df.AddRow("Rue", 39);
df.AddRow("Peeta", 78);
df.PrimaryKey = df["name"];
DataFrame df2 = df[df["age"] > 40];
// DataFrame df2 = df[df["age"] > 40 & df["name"] == "Michael"];
Console.WriteLine(df);
Console.WriteLine("======================");
Console.WriteLine(df2);
Console.WriteLine();
// df.SaveToFile("test_df.cdf", Encoding.UTF8);
Console.WriteLine("Finished.");
}
public void TestAddRow()
{
var d = new DataFrame();
Assert.AreEqual(0, d.Count);
var obj = new PSObject();
obj.Properties.Add(new PSNoteProperty("abc", 123));
obj.Properties.Add(new PSNoteProperty("xyz", "aaa"));
d.AddRow(obj);
var obj2 = new PSObject();
obj2.Properties.Add(new PSNoteProperty("abc", 456));
obj2.Properties.Add(new PSNoteProperty("xyz", "bbb"));
d.AddRow(obj2);
Assert.AreEqual(2, d.Count);
Assert.AreEqual(123, d.GetRow(0).Properties["abc"].Value);
Assert.AreEqual("bbb", d.GetRow(1).Properties["xyz"].Value);
}
private DataFrame GetTestFrame()
{
DataFrame frame = new DataFrame(
new DataHeader <string>("name"),
new DataHeader <double>("height"),
new DataHeader <bool?>("male")
);
frame.AddRow("a", 7.0, false);
frame.AddRow(null, 6.5, true);
frame.AddRow("c", 6.0, false);
frame.AddRow("d", 5.5, true);
frame.AddRow("e", 5.0, null);
frame.AddRow("f", 4.5, true);
return(frame);
}
public static int Main(string[] args)
{
string modelDirectory = ((args != null) && (args.Length > 0)) ? args[0]
: "../../models";
string solver = ((args != null) && (args.Length > 1)) ? args[1] : null;
// Create first dataframe (for data indexed over NUTR) Add data row by row
DataFrame df1 = new DataFrame(1, "NUTR", "n_min", "n_max");
df1.AddRow("A", 700, 20000);
df1.AddRow("B1", 700, 20000);
df1.AddRow("B2", 700, 20000);
df1.AddRow("C", 700, 20000);
df1.AddRow("CAL", 16000, 24000);
df1.AddRow("NA", 0.0, 50000);
// Create second dataframe (for data indexed over FOOD) Add column by column
DataFrame df2 = new DataFrame(1, "FOOD");
string[] foods = { "BEEF", "CHK", "FISH", "HAM",
"MCH", "MTL", "SPG", "TUR" };
df2.SetColumn("FOOD", foods);
double[] contents = new double[8];
for (int j = 0; j < 8; j++)
{
contents[j] = 2;
}
df2.AddColumn("f_min", contents);
for (int j = 0; j < 8; j++)
{
contents[j] = 10;
}
df2.AddColumn("f_max", contents);
double[] costs = { 3.19, 2.59, 2.29, 2.89, 1.89,
1.99, 1.99, 2.49 };
df2.AddColumn("cost", costs);
// Create third dataframe, to assign data to the AMPL entity param amt{NUTR, FOOD};
DataFrame df3 = new DataFrame(2, "NUTR", "FOOD");
// Populate the set columns
string[] nutrWithMultiplicity = new string[48];
string[] foodWithMultiplicity = new string[48];
int i = 0;
for (int n = 0; n < 6; n++)
{
for (int f = 0; f < 8; f++)
{
nutrWithMultiplicity[i] = df1.GetRowByIndex(n)[0].Str;
foodWithMultiplicity[i++] = foods[f];
}
}
df3.SetColumn("NUTR", nutrWithMultiplicity);
df3.SetColumn("FOOD", foodWithMultiplicity);
// Populate with all these values
double[] values = { 60, 8, 8, 40, 15, 70, 25, 60, 10, 20, 15,
35, 15, 15, 25, 15, 15, 20, 10, 10, 15, 15, 15, 10, 20, 0, 10,
40, 35, 30, 50, 20, 295, 770, 440, 430, 315, 400, 370, 450,
968, 2180, 945, 278, 1182, 896, 1329, 1397 };
df3.AddColumn("amt", values);
// Create an AMPL instance
using (AMPL ampl = new AMPL())
{
if (solver != null)
{
ampl.SetOption("solver", solver);
}
// Read model only
ampl.Read(modelDirectory + "/diet/diet.mod");
// Assign data to NUTR, n_min and n_max
ampl.SetData(df1, "NUTR");
// Assign data to FOOD, f_min, f_max and cost
ampl.SetData(df2, "FOOD");
// Assign data to amt
ampl.SetData(df3);
// Solve the model
ampl.Solve();
// Print out the result
Console.Write("Objective function value: {0}\n",
ampl.GetObjective("Total_Cost").Value);
// Get the values of the variable Buy in a dataframe
DataFrame results = ampl.GetVariable("Buy").GetValues();
// Print
Console.WriteLine(results.ToString());
}
return(0);
}
