public void EqualsTestEqualsOperator()
{
TermCountQuery o1 = new TermCountQuery();
o1.Limit = 1;
TermCountQuery o2 = new TermCountQuery();
o2.Limit = 1;
Assert.IsTrue(o1 == o2);
}
public void EqualsTestGetHasCodesEquals()
{
TermCountQuery o1 = new TermCountQuery();
o1.Limit = 1;
TermCountQuery o2 = new TermCountQuery();
o2.Limit = 1;
Assert.AreEqual(o1.GetHashCode(), o2.GetHashCode());
}
public void EqualsTestEquality()
{
TermCountQuery o1 = new TermCountQuery();
o1.Limit = 1;
TermCountQuery o2 = new TermCountQuery();
o2.Limit = 1;
Assert.IsTrue(o1.Equals(o2));
}
public void CategoryIdsTest()
{
TermCountQuery q = new TermCountQuery();
List<int> categoryIds = new List<int>() { 1, 2, 3, 4 };
q.CategoryIds = categoryIds;
Assert.AreEqual(categoryIds, q.CategoryIds);
}
public void GetIndividualTermCountTestPerformanceImprovementOneTerm()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Set some news as interesting.
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[0].Title)), true);
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[1].Title)), true);
// Calculate the expected term frequency of all terms
// in the two interesting news.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
Dictionary<string, int> tf1 =
TermUtils.CalculateTermFrequency(NewsMaterial[1].Content);
// Combine the two dictionaries.
foreach (KeyValuePair<string, int> t in tf1)
{
if (tf0.ContainsKey(t.Key))
{
// Increase frequency.
tf0[t.Key] += t.Value;
}
else
{
// Add term with frequency.
tf0.Add(t.Key, t.Value);
}
}
// Expected value is equal to tf0
Dictionary<string, int> expectedDictionary = new Dictionary<string, int>();
expectedDictionary.Add(tf0.Keys.ElementAt(0), tf0[tf0.Keys.ElementAt(0)]);
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Interest = InterestStatus.Interesting
};
tcq.Terms.Add(tf0.Keys.ElementAt(0));
Dictionary<string, int> actualDictionary = Archivist.GetIndividualTermCount(tcq);
CollectionAssert.AreEquivalent(expectedDictionary, actualDictionary);
}
public void GetIndividualTermCountTestOffset()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Calculate the expected term frequency of terms in all news items.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
Dictionary<string, int> tf1 =
TermUtils.CalculateTermFrequency(NewsMaterial[1].Content);
// Combine the dictionaries
foreach (KeyValuePair<string, int> t in tf1)
{
if (tf0.ContainsKey(t.Key))
{
// Increase frequency.
tf0[t.Key] += t.Value;
}
else
{
// Add term with frequency.
tf0.Add(t.Key, t.Value);
}
}
// Copy the keys to a list.
List<string> termsToRemove = tf0.Keys.ToList();
// Remove everything but the first 50 elements.
termsToRemove.RemoveRange(50, termsToRemove.Count - 50);
// Remove the strings that should not be in the dictionary.
foreach (string term in termsToRemove)
{
tf0.Remove(term);
}
Dictionary<string, int> expectedDictionary = tf0;
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Offset = 50
};
Dictionary<string, int> actualDictionary = Archivist.GetIndividualTermCount(tcq);
CollectionAssert.AreEquivalent(expectedDictionary, actualDictionary);
}
public void EqualsTestObjectToNullReferenceEqualsOperator()
{
TermCountQuery o1 = new TermCountQuery();
o1.Limit = 1;
TermCountQuery o2 = null;
Assert.IsFalse(o1 == o2);
}
/// <summary>
/// Gets the individual term frequency of all terms matching the
/// given <c>TermCountQuery</c>.
/// </summary>
/// <param name="termCountQuery">
/// A <c>TermCountQuery</c> specifying the terms to get the term
/// frequency of.
/// </param>
/// <returns>
/// A <c>Dictionary</c> with the key being a term and the value being
/// its frequency matching the <c>TermCountQuery</c>.
/// </returns>
public abstract Dictionary<string, int> GetIndividualTermCount(
TermCountQuery termCountQuery);
public void RemoveNewsTestTermStays()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Adds a custom unique term to two of the newsmaterials.
string testTerm = "FooBarDeadWhale";
string stemmedTestTerm = (new Porter2()).stem(testTerm);
NewsMaterial[0].Content += " " + testTerm + ".";
NewsMaterial[1].Content += " " + testTerm + ".";
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Request to count the number of testTerm in the databae.
TermCountQuery termRequest = new TermCountQuery();
termRequest.Terms.Add(stemmedTestTerm);
// Remove an item.
Archivist.RemoveNews(gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[0].Title)));
// The actual ammount of terms.
int actual = Archivist.GetTermCount(termRequest);
// The expected ammount of terms.
int expected = 1;
Assert.AreEqual(expected, actual);
}
public void HasDefaultValues()
{
TermCountQuery q = new TermCountQuery();
// Expected values.
InterestStatus interest = InterestStatus.Any;
ReadStatus read = ReadStatus.Any;
int limit = 100000;
int offset = 0;
List<int> categoryIds = new List<int>();
List<string> terms = new List<string>();
// Check the default values.
Assert.AreEqual(interest, q.Interest,
"Interest is not default. Expected " + interest + " but got " +
q.Interest + ".");
Assert.AreEqual(read, q.Read,
"Read is not default. Expected " + read + " but got " +
q.Read + ".");
Assert.AreEqual(limit, q.Limit, "Limit is not default. Expected " +
limit + " but got " + q.Limit + ".");
Assert.AreEqual(offset, q.Offset, "Offset is not default. Expected " +
offset + " but got " + q.Offset + ".");
CollectionAssert.AreEquivalent(categoryIds, q.CategoryIds,
"CategoryIds is not default.");
CollectionAssert.AreEquivalent(terms, q.Terms,
"Terms is not default.");
// Hard to test as the DateTime for the creation of q cannot
// be determined. Therefor the type is checked.
Assert.IsInstanceOfType(q.NewerThan, typeof(DateTime),
"NewerThan is not a DateTime.");
}
public void GetTermCountTestNewerThan()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Calculate the term frequency of the expected news item.
Dictionary<string, int> tf2 =
TermUtils.CalculateTermFrequency(NewsMaterial[2].Content);
// Find the total number of terms in the news items.
int expectedTermCount = 0;
foreach (KeyValuePair<string, int> t in tf2)
{
expectedTermCount += t.Value;
}
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
NewerThan = new DateTime(2013, 4, 27, 12, 50, 00)
};
int actualTermCount = Archivist.GetTermCount(tcq);
Assert.AreEqual(expectedTermCount, actualTermCount);
}
public void TermsTest()
{
TermCountQuery q = new TermCountQuery();
List<string> terms = new List<string>() { "fnyld", "dyld", "badyld" };
q.Terms = terms;
CollectionAssert.AreEquivalent(terms, q.Terms);
}
public void ReadTest()
{
TermCountQuery q = new TermCountQuery();
ReadStatus read = ReadStatus.Read;
q.Read = read;
Assert.AreEqual(read, q.Read);
}
public void OffsetTest()
{
TermCountQuery q = new TermCountQuery();
int offset = 98;
q.Offset = offset;
Assert.AreEqual(offset, q.Offset);
}
public void NewerThanTest()
{
TermCountQuery q = new TermCountQuery();
DateTime newerThan = DateTime.Now;
q.NewerThan = newerThan;
Assert.AreEqual(newerThan, q.NewerThan);
}
public void InterestTest()
{
TermCountQuery q = new TermCountQuery();
InterestStatus interest = InterestStatus.Uninteresting;
q.Interest = interest;
Assert.AreEqual(interest, q.Interest);
}
public void GetIndividualTermCountTestPerformanceImprovementTwoTermsSpecificCategory()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Set some news as interesting.
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[0].Title)), true);
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[2].Title)), true);
// Calculate the expected term frequency of all terms
// in the interesting news item from category 0.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
// Expected value is equal to tf0.
Dictionary<string, int> expectedDictionary = new Dictionary<string, int>();
expectedDictionary.Add(tf0.Keys.ElementAt(0), tf0[tf0.Keys.ElementAt(0)]);
expectedDictionary.Add(tf0.Keys.ElementAt(1), tf0[tf0.Keys.ElementAt(1)]);
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Interest = InterestStatus.Interesting
};
tcq.Terms.Add(tf0.Keys.ElementAt(0));
tcq.Terms.Add(tf0.Keys.ElementAt(1));
tcq.CategoryIds.Add(Categories[0].Id);
Dictionary<string, int> actualDictionary = Archivist.GetIndividualTermCount(tcq);
CollectionAssert.AreEquivalent(expectedDictionary, actualDictionary);
}
public void GetTermCountTestOffset()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Calculate the expected term frequency of terms in all news items.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
Dictionary<string, int> tf1 =
TermUtils.CalculateTermFrequency(NewsMaterial[1].Content);
// Combine the dictionaries
foreach (KeyValuePair<string, int> t in tf1)
{
if (tf0.ContainsKey(t.Key))
{
// Increase frequency.
tf0[t.Key] += t.Value;
}
else
{
// Add term with frequency.
tf0.Add(t.Key, t.Value);
}
}
// Copy the keys to a list.
List<string> terms = tf0.Keys.ToList();
// Remove the first 50 terms.
terms.RemoveRange(0, 50);
// Find the total number of terms in the news items.
int expectedTermCount = 0;
foreach (KeyValuePair<string, int> t in tf0)
{
if (terms.Contains(t.Key))
{
expectedTermCount += t.Value;
}
}
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Offset = 50
};
int actualTermCount = Archivist.GetTermCount(tcq);
Assert.AreEqual(expectedTermCount, actualTermCount);
}
public void GetTermCountTestMisc()
{
// The term to search for.
string termToSearchFor1 = "poni";
string termToSearchFor2 = "vermont";
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Set some news as interesting.
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[0].Title)), true);
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[1].Title)), true);
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[3].Title)), true);
// Set some news as read.
Archivist.SetNewsReadStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[0].Title)), true);
// Calculate the expected term frequency.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
// Find the frequency of the term to search for.
int expectedTermCount = 0;
foreach (KeyValuePair<string, int> t in tf0)
{
if (t.Key.Equals(termToSearchFor1))
{
expectedTermCount += t.Value;
}
}
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Interest = InterestStatus.Interesting,
Read = ReadStatus.Read,
CategoryIds = new List<int>() { Categories[0].Id },
Terms = new List<string>()
{
termToSearchFor1,
termToSearchFor2
}
};
int actualTermCount = Archivist.GetTermCount(tcq);
Assert.AreEqual(expectedTermCount, actualTermCount);
}
public void GetTermCountTestRead()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Set some news as read.
Archivist.SetNewsReadStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[1].Title)), true);
Archivist.SetNewsReadStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[2].Title)), true);
// Calculate the expected term frequency of all terms
// in the two read news.
Dictionary<string, int> tf1 =
TermUtils.CalculateTermFrequency(NewsMaterial[1].Content);
Dictionary<string, int> tf2 =
TermUtils.CalculateTermFrequency(NewsMaterial[2].Content);
// Combine the two dictionaries.
foreach (KeyValuePair<string, int> t in tf2)
{
if (tf1.ContainsKey(t.Key))
{
// Increase frequency.
tf1[t.Key] += t.Value;
}
else
{
// Add term with frequency.
tf1.Add(t.Key, t.Value);
}
}
// Find the total number of terms in the news items.
int expectedTermCount = 0;
foreach (KeyValuePair<string, int> t in tf1)
{
expectedTermCount += t.Value;
}
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Read = ReadStatus.Read
};
int actualTermCount = Archivist.GetTermCount(tcq);
Assert.AreEqual(expectedTermCount, actualTermCount);
}
public void GetTermCountTestNoTermsFetched()
{
// The term to search for.
string termToSearchFor1 = "poni";
string termToSearchFor2 = "vermont";
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Set some news as interesting.
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[1].Title)), true);
Archivist.SetNewsInterestingStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[3].Title)), true);
// Set some news as read.
Archivist.SetNewsReadStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[0].Title)), true);
Archivist.SetNewsReadStatus(
gottenNewsItems.Find(n => n.Title.Equals(NewsMaterial[3].Title)), true);
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Interest = InterestStatus.Interesting,
Read = ReadStatus.Read,
CategoryIds = new List<int>() { Categories[0].Id },
Terms = new List<string>()
{
termToSearchFor1,
termToSearchFor2
}
};
int actualTermCount = Archivist.GetTermCount(tcq);
Assert.AreEqual(0, actualTermCount);
}
public void EqualsTestReferenceEquals()
{
TermCountQuery o1 = new TermCountQuery();
o1.Limit = 1;
TermCountQuery o2 = o1;
Assert.IsTrue(o1.Equals(o2));
}
public void GetTermCountTestOneTerm()
{
// The term to search for.
string termToSearchFor = "poni";
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Calculate the term frequency of news items with the term
// to search for.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
// Find the frequency of the term to search for.
int expectedTermCount = 0;
foreach (KeyValuePair<string, int> t in tf0)
{
if (t.Key.Equals(termToSearchFor))
{
expectedTermCount += t.Value;
}
}
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Terms = new List<string>() { termToSearchFor }
};
int actualTermCount = Archivist.GetTermCount(tcq);
Assert.AreEqual(expectedTermCount, actualTermCount);
}
public void EqualsTestInequality()
{
TermCountQuery o1 = new TermCountQuery();
o1.Limit = 1;
TermCountQuery o2 = new TermCountQuery();
o1.Limit = 2;
Assert.IsFalse(o1.Equals(o2));
}
public void GetTermCountTestTwoTerms()
{
// The term to search for.
string termToSearchFor1 = "poni";
string termToSearchFor2 = "vermont";
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Calculate the expected term frequency.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
Dictionary<string, int> tf1 =
TermUtils.CalculateTermFrequency(NewsMaterial[1].Content);
// Combine the two dictionaries.
foreach (KeyValuePair<string, int> t in tf1)
{
if (tf0.ContainsKey(t.Key))
{
// Increase frequency.
tf0[t.Key] += t.Value;
}
else
{
// Add term with frequency.
tf0.Add(t.Key, t.Value);
}
}
// Find the frequency of the term to search for.
int expectedTermCount = 0;
foreach (KeyValuePair<string, int> t in tf0)
{
if (t.Key.Equals(termToSearchFor1) ||
t.Key.Equals(termToSearchFor2))
{
expectedTermCount += t.Value;
}
}
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
Terms = new List<string>()
{
termToSearchFor1,
termToSearchFor2
}
};
int actualTermCount = Archivist.GetTermCount(tcq);
Assert.AreEqual(expectedTermCount, actualTermCount);
}
public void GetIndividualTermCountTestCategoryIds()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Calculate the expected term frequency.
Dictionary<string, int> tf0 =
TermUtils.CalculateTermFrequency(NewsMaterial[0].Content);
Dictionary<string, int> tf1 =
TermUtils.CalculateTermFrequency(NewsMaterial[1].Content);
// Combine the two dictionaries.
foreach (KeyValuePair<string, int> t in tf1)
{
if (tf0.ContainsKey(t.Key))
{
// Increase frequency.
tf0[t.Key] += t.Value;
}
else
{
// Add term with frequency.
tf0.Add(t.Key, t.Value);
}
}
Dictionary<string, int> expectedDictionary = tf0;
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
CategoryIds = new List<int> { Categories[0].Id }
};
Dictionary<string, int> actualDictionary = Archivist.GetIndividualTermCount(tcq);
CollectionAssert.AreEquivalent(expectedDictionary, actualDictionary);
}
/// <summary>
/// Counts the number of documents satisfying the parameter values.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> instance with database access.
/// </param>
/// <param name="terms">
/// A list of terms the documents should contain.
/// </param>
/// <param name="category">
/// A <c>Category</c> the documents should belong to.
/// </param>
/// <param name="interesting">
/// Whether the documents should be marked interesting or not.
/// </param>
/// <returns>
/// The number of documents satisfying the parameter values.
/// </returns>
private Dictionary<string, int> CountTermsWithQuery(Archivist archivist, List<string> terms,
Category category, bool interesting)
{
// Create query instance.
TermCountQuery termQuery = new TermCountQuery();
// Add term to query if not null.
if (terms != null)
{
termQuery.Terms = terms;
}
// Set category if not null.
if (category != null)
{
termQuery.CategoryIds = new List<int> { category.Id };
}
termQuery.Interest = interesting ?
Database.InterestStatus.Interesting :
Database.InterestStatus.Uninteresting;
// Send query and return result.
return archivist.GetIndividualTermCount(termQuery);
}
public void GetIndividualTermCountTestNewerThan()
{
// Add categories and news sources.
foreach (Category c in Categories)
{
c.Id = Archivist.AddCategory(c.Name);
}
Archivist.AddNewsSources(NewsSources);
// Add news with forced categories.
Archivist.AddNews(NewsMaterial[0], Categories[0].Id);
Archivist.AddNews(NewsMaterial[1], Categories[0].Id);
Archivist.AddNews(NewsMaterial[2], Categories[1].Id);
Archivist.AddNews(NewsMaterial[3], Categories[2].Id);
// Get all the news items.
List<NewsItem> gottenNewsItems = Archivist.GetNews(new NewsQuery());
// Calculate the term frequency of the expected news item.
Dictionary<string, int> tf2 =
TermUtils.CalculateTermFrequency(NewsMaterial[2].Content);
Dictionary<string, int> expectedDictionary = tf2;
// Create a term count query.
TermCountQuery tcq = new TermCountQuery()
{
NewerThan = new DateTime(2013, 4, 27, 12, 50, 00)
};
Dictionary<string, int> actualDictionary = Archivist.GetIndividualTermCount(tcq);
CollectionAssert.AreEquivalent(expectedDictionary, actualDictionary);
}
/// <summary> /// Gets the sum of the term frequencies of all the terms that /// match the given <c>TermCountQuery</c>. /// </summary> /// <param name="termCountQuery"> /// A <c>TermCountQuery</c> specifying the terms to get the term /// frequency of. /// </param> /// <returns> /// The sum of the term frequencies of all the terms that match /// the given <c>TermCountQuery</c>. /// </returns> public abstract int GetTermCount(TermCountQuery termCountQuery);
public void EqualsTestObjectReferenceEquality()
{
TermCountQuery o1 = new TermCountQuery();
o1.Limit = 1;
TermCountQuery o2 = new TermCountQuery();
o2.Limit = 1;
Assert.IsTrue(o1.Equals((Object) o2));
}
