/// <summary>
/// save strict result object to JSON file (MultiAprioriResult)
/// </summary>
/// <param name="res"></param>
/// <param name="pretty"></param>
public SimpleJsonSaver(Ceka.Algorithms.Associaters.MultithreadedApriori.NestedFastAprioriResult res, bool pretty = false)
{
if (!pretty)
this.value = JsonConvert.SerializeObject(res);
else
this.value = JsonConvert.SerializeObject(res, Formatting.Indented);
}
/// <summary>
/// save strict result object to JSON file (SVMResult)
/// </summary>
/// <param name="res"></param>
/// <param name="pretty"></param>
public SimpleJsonSaver(Ceka.Algorithms.Classifiers.SupportVectorMachine.NestedSVMResult res, bool pretty = false)
{
if (!pretty)
this.value = JsonConvert.SerializeObject(res);
else
this.value = JsonConvert.SerializeObject(res, Formatting.Indented);
}
/// <summary>
/// save strict result object to JSON file (KMeansResult)
/// </summary>
/// <param name="res"></param>
/// <param name="pretty"></param>
public SimpleJsonSaver(Ceka.Algorithms.Clusterers.KMeans.NestedKMeansResult res, bool pretty = false)
{
if (!pretty)
this.value = JsonConvert.SerializeObject(res);
else
this.value = JsonConvert.SerializeObject(res, Formatting.Indented);
}
/// <summary>
/// trys to mimic the output of Weka's Associator Rule Results (humanreadable) - AprioriResult
/// </summary>
/// <param name="result"></param>
public WekaAssociationRulesSaver(Ceka.Algorithms.Associaters.Apriori.NestedAprioriResult result)
{
StringBuilder sb = new StringBuilder();
sb.Append("=== Run information === \n\n");
sb.Append("Scheme: Ceka.Algorithms.Associators.Apriori \n");
sb.Append("Minimum Support Filter Active: ");
sb.Append(result.SupportApplied);
sb.Append("\n");
sb.Append("Confidence Filter Active: ");
sb.Append(result.ConfidenceApplied);
sb.Append("\n");
sb.Append("Relation: ");
sb.Append(result.Relation);
sb.Append("\n");
sb.Append("Instances: ");
sb.Append(result.TotalDatasets);
sb.Append("\n");
sb.Append("Attributes: ");
sb.Append(result.Attributes.Count);
sb.Append("\n");
foreach (string s in result.Attributes)
{
sb.Append("\t");
sb.Append(s);
sb.Append("\n");
}
sb.Append("Attributes of Value: ");
sb.Append(result.ItemTable.Count);
sb.Append("\n");
foreach (List<object> at in result.ItemTable)
{
sb.Append("\t");
foreach (object o in at)
{
sb.Append(o);
sb.Append(" ");
}
sb.Append("\n");
}
sb.Append("=== Associator model (full training set) ===\n\n\n");
sb.Append("Apriori\n=======\n\n");
sb.Append("Minimum support: ");
sb.Append(result.MinimumSupport);
sb.Append(" (");
sb.Append((int)(result.TotalDatasets * result.MinimumSupport));
sb.Append(" instances)\n");
sb.Append("Minimum metric <confidence>: ");
sb.Append(result.Confidence);
sb.Append("\nNumber of cycles performed: ");
sb.Append(result.CyclesRan);
sb.Append("\n\nGenerated sets of large itemsets:\n\n");
int lloc = 1;
foreach (List<List<object>> ds in result.CycleResults)
{
sb.Append("Size of set of large itemsets L(");
sb.Append(lloc);
sb.Append("): ");
sb.Append(ds.Count);
sb.Append("\n\n");
lloc++;
}
sb.Append("Best rules found:\n\n");
int line = 1;
for (int i = result.CycleResults.Count - 1; i >= 0; i--)
{
foreach (List<object> dsl in result.CycleResults[i])
{
sb.Append(line);
sb.Append(". ");
for (int d = 0; d < dsl.Count - 2; d++)
{
sb.Append(dsl[d]);
sb.Append(" ");
if (d == dsl.Count -4)
sb.Append("==> ");
}
sb.Append(" conf:(");
sb.Append(float.Parse(dsl[dsl.Count - 1].ToString()) / 1000);
sb.Append(")\n");
line++;
//dsl[dsl.Count - 1] = confidence
//dsl[dsl.Count - 2] = support
}
}
this.value = sb.ToString();
}
