I use this code to create a new iteration on VSTS/TFS programatically:
var tfs = new TfsTeamProjectCollection(uri, tfsCredential);
var service = tfs.GetService<ICommonStructureService>();
var iterationRoot = service.GetNodeFromPath("\\TeamProjectName\\Iteration");
var iteration = service.CreateNode("Sprint 1", iterationRoot.Uri);
Now I want to remove an iteration but there is no corresponding method on ICommonStructureService. Oddly there is a method named GetDeletedNodesXml().
I got it! I'm assuming here that you have some method to retrieve an iteration.
var tfs = new TfsTeamProjectCollection(uri, tfsCredential);
var service = tfs.GetService<ICommonStructureService>();
// TODO var iteration = GetIteration();
var projectInfo = service.GetProjectFromName(projectName)
var nodes = service.ListStructures(projectInfo.Uri);
service.DeleteBranches(iteration.Id, nodes[0].Uri);
The key is to pass in Ids and not paths. TFS wants to retrieve artifact URLs which are represented as Ids. The second parameter of DeleteBranches is the artifact URL of the iteration-root which is obtained by calling ListStructures of the ICommonStructureService and taking the first element there (which is kind of nasty IMHO but I don't know a better way).
Related
I am using nHapi v2.4 to build HL7 message.
I would like to copy the Ordering provider from ORC segment to Ordering provider in OBR segment.
I am trying to do it in the following way but it doesn't work:
// I have created objects in the following way.
ORM_O01 _ormMessage = new ORM_O01()
ORM_O01_ORDER order = _ormMessage.AddORDER();
var obrSegment = order.ORDER_DETAIL.OBR;
var orcSegment = order.ORC;
// Here, Set ordering field in ORC segment.
// Now, set Ordering provider in OBR
foreach(var orcOrderingProvider in orcSegment.GetOrderingProvider())
{
var obrOrderingProvider = obrSegment.GetOrderingProvider(obrSegment.OrderingProviderRepetitionsUsed);
obrOrderingProvider = orcOrderingProvider;
}
Is there any simple way to copy the whole field from one field to another?
Thank you.
When adding records to a set, the resulting keys variable contains only one empty string, instead of the expect "i1".
var workspace = new GAMSWorkspace("TestWorkspace");
var database = workspace.AddDatabase();
var set = database.AddSet("TestSet", 1);
var record = set.AddRecord("i1");
var keys = record.Keys;
database.Export("TestDb");
What can cause this problem running the version Assembly GAMS.net4, Version=28.2.0.0
When I export the database to a .gdx file, the set contains the element i1.
How did you check, that the keysvariable does not contain the expected string? The record.Keys property returns a string[]. And if I add a WriteLine to your example, I get the expected string:
var database = ws.AddDatabase();
var set = database.AddSet("TestSet", 1);
var record = set.AddRecord("i1");
var keys = record.Keys;
Console.WriteLine(keys[0]);
Output:
i1
I am trying to bulk a collection of elements inside an index of ElasticSearch using NEST inside a .NET Core application.
Currently what I have is working, and the elements are saved, but Is not saved where I try to do
My client creation:
protected ElasticClient GetClient()
{
var node = new Uri("http://localhost:9200/");
var settings = new ConnectionSettings(node)
.DefaultIndex("TestIndex")
.PrettyJson(true);
return new ElasticClient(settings);
}
Here is how I create the descriptor for bulk all the data
protected BulkDescriptor GenerateBulkDescriptor<T>(IEnumerable<T> elements, string indexName) where T: class, IIndexable
{
var bulkIndexer = new BulkDescriptor();
foreach (var element in elements)
bulkIndexer.Index<T>(i => i
.Document(element)
.Id(element.Id)
.Index(indexName));
return bulkIndexer;
}
Finally, once I have this, here is how I index the data
var descriptor = GenerateBulkDescriptor(indexedElements, "indexed_elements");
var response = GetClient().Bulk(descriptor);
But, If I see how It's stored in the Elastic index using this, that is what I have:
How can I know if is created under TestIndex index? Because as far as I can see, there is just one index created
Thank you a lot in advance
When defining the index operations on the BulkDescriptor, you are explicitly setting the index to use for each operation
foreach (var element in elements)
bulkIndexer.Index<T>(i => i
.Document(element)
.Id(element.Id)
.Index(indexName));
where indexName is "indexed_elements". This is why all documents are indexed into this index and you do not see any in "TestIndex".
The Bulk API allows multiple operations to be defined, which may include indexing documents into different indices. When the index is specified directly on an operation, that will be the index used. If all index operations on a Bulk API call are to take place against the same index, you can omit the index on each operation and instead, specify the index to use on the Bulk API call directly
var defaultIndex = "default_index";
var pool = new SingleNodeConnectionPool(new Uri("http://localhost:9200"));
var settings = new ConnectionSettings(pool)
.DefaultIndex(defaultIndex);
var client = new ElasticClient(settings);
var people = new []
{
new Person { Id = 1, Name = "Paul" },
new Person { Id = 2, Name = "John" },
new Person { Id = 3, Name = "George" },
new Person { Id = 4, Name = "Ringo" },
};
var bulkResponse = client.Bulk(b => b
.Index("people")
.IndexMany(people)
);
which sends the following request
POST http://localhost:9200/people/_bulk
{"index":{"_id":"1","_type":"person"}}
{"id":1,"name":"Paul"}
{"index":{"_id":"2","_type":"person"}}
{"id":2,"name":"John"}
{"index":{"_id":"3","_type":"person"}}
{"id":3,"name":"George"}
{"index":{"_id":"4","_type":"person"}}
{"id":4,"name":"Ringo"}
Note that the URI is /people/bulk and that each JSON object representing an operation does not contain an "_index".
If you omit the .Index() on Bulk API call, it will use the DefaultIndex configured on ConnectionSettings:
var bulkResponse = client.Bulk(b => b
.IndexMany(people)
);
which yields
POST http://localhost:9200/_bulk
{"index":{"_id":"1","_index":"default_index","_type":"person"}}
{"id":1,"name":"Paul"}
{"index":{"_id":"2","_index":"default_index","_type":"person"}}
{"id":2,"name":"John"}
{"index":{"_id":"3","_index":"default_index","_type":"person"}}
{"id":3,"name":"George"}
{"index":{"_id":"4","_index":"default_index","_type":"person"}}
{"id":4,"name":"Ringo"}
You can also specify a default index to use for a given POCO type on ConnectionSettings with DefaultMappingFor<T>(), where T is your POCO type.
After som tests and attemps, I have found a solution.
First of all, it was a problem with the index configured, once I set it in lower case, the index was working fine indexing data inside.
Then, I had the problem of index data in a specific "path" inside the same index, finalyy I found the Type solution from NEST, taking also advantage of the DefaultMappingFor suggested by Russ in the previous answer.
Client definition:
var node = new Uri(_elasticSearchConfiguration.Node);
var settings = new ConnectionSettings(node)
.DefaultMappingFor<IndexedElement>(m => m
.IndexName(_elasticSearchConfiguration.Index)
.TypeName(nameof(IndexedElement).ToLower()))
.PrettyJson(true)
.DisableDirectStreaming();
var client = new ElasticClient(settings);
Then, the BulkDescriptior creation:
var bulkIndexer = new BulkDescriptor();
foreach (var element in elements)
bulkIndexer.Index<IndexedElement>(i => i
.Document(element)
.Type(nameof(IndexedElement).ToLower()))
.Id(element.Id)
);
And finally, data bulk:
client.Bulk(bulkIndexer);
Now, If I perform a call to the index, I can see this
{
"testindex": {
"aliases": {},
"mappings": {
"indexedelement": {
[...]
}
Thank you Russ for your help and for who have had a look to the post.
UPDATE
Finally, it seems that the unique problem was regarding the default index, that it must be lowercase, so, specify the type with the name of the POCO itself is not neccesary, like #RussCam has truly detected in comments above. After changing thedefault index to lowercase, all the different possibilities worked fine.
Thank you all again
I'm trying to port over JavaScript code that interacts with a MongoDb to C# .NET and having problems with a delete/pull operation. I can't seem to figure out how to iterate over the Ponies array and remove a single ObjectId by matching to a passed in ObjectId.
{
"_id" : ObjectId("388e8a66fe2af4e35c60e"),
"Location" : "rainbowland",
"Ponies" : [
ObjectId("388e8a66fe2af4e35c24e83c"),
ObjectId("388e8a66fe2af4e35c24e860"),
ObjectId("388e8a66fe2af4e35c24e83d")
]
}
I've tried several different ways based on what I've found online but nothing seems to work. When I check the collection to see if 388e8a66fe2af4e35c24e860 was removed from the Ponies array I see that it wasn't removed. Everytime I execute the code and look at the ModifiedCount for resultPonies it shows zero. Two examples of what I've tried doing are below.
var pony = new Pony() { Id = new ObjectId("388e8a66fe2af4e35c24e860") }
var pullPonies = Builders<Ranch>.Update.PullFilter(x => x.Ponies,
y => y.Id == pony.Id);
var filterPonies = Builders<Ranch>.Filter.Where(x => true);
var resultPonies = _context.Ranches.UpdateMany(filterPonies, pullPonies);
OR
var pullPonies = Builders<BsonDocument>.Update.PullFilter("Ponies",
Builders<ObjectId>.Filter.Eq("ObjectId", pony.Id));
var filterPonies = Builders<BsonDocument>.Filter.Where(x => true);
var resultPonies = _context.Ranches.UpdateMany(filterPonies, pullPonies);
Here is the JavaScript code that I believe works but haven't been able to test yet.
db.collection("Ranches").update({}, {$pull: {Ponies: pony._id}}, {multi: true});
Ok, I found a solution for the problem. Since I'm not actually going to be filtering within the array of objects, they are all ObjectIds in a BsonArray, I need to find where the value in the array matches the given ObjectId. In those cases the array item should be pulled out. If the object in the array had other properties then I would probably want to use the PullFilter. But in this case I don't need to filter the additional level. That is at least what I believe I was doing wrong. Here are the solutions
var ponyId = new ObjectId("388e8a66fe2af4e35c24e860");
var pullPonies = Builders<Ranch>.Update.Pull(x => x.Ponies, ponyId);
// This line is to retrieve all Ranch objects in the collection
var filterPonies = Builders<Ranch>.Filter.Where(x => true);
var resultPonies = _context.Ranches.UpdateMany(filterPonies, pullPonies);
Or
var ponyId = new ObjectId("388e8a66fe2af4e35c24e860");
var pullPonies = Builders<BsonDocument>.Update.Pull("Ponies", ponyId));
// This line is to retrieve all Ranch objects in the collection
var filterPonies = Builders<BsonDocument>.Filter.Where(x => true);
var resultPonies = _context.Ranches.UpdateMany(filterPonies, pullPonies);
I'm trying to build a prediction module implementing a Hidden Markov Model type DBN in Bayes Server 7 C#. I managed to create the network structure but I'm not sure if its correct because their documentation and examples are not very comprehensive and I also don't fully understand how the prediction is meant to be done in the code after training is complete.
Here is a how my Network creation and training code looks:
var Feature1 = new Variable("Feature1", VariableValueType.Continuous);
var Feature2 = new Variable("Feature2", VariableValueType.Continuous);
var Feature3 = new Variable("Feature3", VariableValueType.Continuous);
var nodeFeatures = new Node("Features", new Variable[] { Feature1, Feature2, Feature3 });
nodeFeatures.TemporalType = TemporalType.Temporal;
var nodeHypothesis = new Node(new Variable("Hypothesis", new string[] { "state1", "state2", "state3" }));
nodeHypothesis.TemporalType = TemporalType.Temporal;
// create network and add nodes
var network = new Network();
network.Nodes.Add(nodeHypothesis);
network.Nodes.Add(nodeFeatures);
// link the Hypothesis node to the Features node within each time slice
network.Links.Add(new Link(nodeHypothesis, nodeFeatures));
// add a temporal link of order 5. This links the Hypothesis node to itself in the next time slice
for (int order = 1; order <= 5; order++)
{
network.Links.Add(new Link(nodeHypothesis, nodeHypothesis, order));
}
var temporalDataReaderCommand = new DataTableDataReaderCommand(evidenceDataTable);
var temporalReaderOptions = new TemporalReaderOptions("CaseId", "Index", TimeValueType.Value);
// here we map variables to database columns
// in this case the variables and database columns have the same name
var temporalVariableReferences = new VariableReference[]
{
new VariableReference(Feature1, ColumnValueType.Value, Feature1.Name),
new VariableReference(Feature2, ColumnValueType.Value, Feature2.Name),
new VariableReference(Feature3, ColumnValueType.Value, Feature3.Name)
};
var evidenceReaderCommand = new EvidenceReaderCommand(
temporalDataReaderCommand,
temporalVariableReferences,
temporalReaderOptions);
// We will use the RelevanceTree algorithm here, as it is optimized for parameter learning
var learning = new ParameterLearning(network, new RelevanceTreeInferenceFactory());
var learningOptions = new ParameterLearningOptions();
// Run the learning algorithm
var result = learning.Learn(evidenceReaderCommand, learningOptions);
And this is my attempt at prediction:
// we will now perform some queries on the network
var inference = new RelevanceTreeInference(network);
var queryOptions = new RelevanceTreeQueryOptions();
var queryOutput = new RelevanceTreeQueryOutput();
int time = 0;
// query a probability variable
var queryHypothesis = new Table(nodeHypothesis, time);
inference.QueryDistributions.Add(queryHypothesis);
double[] inputRow = GetInput();
// set some temporal evidence
inference.Evidence.Set(Feature1, inputRow[0], time);
inference.Evidence.Set(Feature2, inputRow[1], time);
inference.Evidence.Set(Feature3, inputRow[2], time);
inference.Query(queryOptions, queryOutput);
int hypothesizedClassId;
var probability = queryHypothesis.GetMaxValue(out hypothesizedClassId);
Console.WriteLine("hypothesizedClassId = {0}, score = {1}", hypothesizedClassId, probability);
Here I'm not even sure how to "Unroll" the network properly to get the prediction and what value to assign to the variable "time". If someone can shed some light on how this toolkit works, I would greatly appreciate it. Thanks.
The code looks fine except for the network structure, which should look something like this for an HMM (the only change to your code is the links):
var Feature1 = new Variable("Feature1", VariableValueType.Continuous);
var Feature2 = new Variable("Feature2", VariableValueType.Continuous);
var Feature3 = new Variable("Feature3", VariableValueType.Continuous);
var nodeFeatures = new Node("Features", new Variable[] { Feature1, Feature2, Feature3 });
nodeFeatures.TemporalType = TemporalType.Temporal;
var nodeHypothesis = new Node(new Variable("Hypothesis", new string[] { "state1", "state2", "state3" }));
nodeHypothesis.TemporalType = TemporalType.Temporal;
// create network and add nodes
var network = new Network();
network.Nodes.Add(nodeHypothesis);
network.Nodes.Add(nodeFeatures);
// link the Hypothesis node to the Features node within each time slice
network.Links.Add(new Link(nodeHypothesis, nodeFeatures));
// An HMM also has an order 1 link on the latent node
network.Links.Add(new Link(nodeHypothesis, nodeHypothesis, 1));
It is also worth noting the following:
You can add multiple distributions to 'inference.QueryDistributions' and query them all at once
While it is perfectly valid to set evidence manually and then query, see EvidenceReader, DataReader and either DatabaseDataReader or DataTableDataReader, if you want to execute the query over multiple records.
Check out the TimeSeriesMode on ParameterLearningOptions
If you want the 'Most probable explanation' set queryOptions.Propagation = PropagationMethod.Max; // an extension of the Viterbi algorithm for HMMs
Check out the following link:
https://www.bayesserver.com/docs/modeling/time-series-model-types
An Hidden Markov model (as a Bayesian network) has a discrete latent variable and a number of child nodes. In Bayes Server you can combine multiple variables in a child node, much like a standard HMM. In Bayes Server you can also mix and match discrete/continuous nodes, handle missing data, and add additional structure (e.g. mixture of HMM, and many other exotic models).
Regarding prediction, once you have built the structure from the link above, there is a DBN prediction example at https://www.bayesserver.com/code/
(Note that you can predict an individual variable in the future (even if you have missing data), you can predict multiple variables (joint probability) in the future, you can predict how anomalous the time series is (log-likelihood) and for discrete (sequence) predictions you can predict the most probable sequence.)
It it is not clear, ping Bayes Server Support and they will add an example for you.