// Called by LandMap.GetZones (), returns number of subregions
public int ClusterLocationsAccordKMedoidsPAM(MapPoint[,] points, TerrainVerticesDatabase vertDatabase)
{
// K-means cluster algorithm to separate locations in the regions
int regionId = 0;
for (int isleId = 0; isleId < regions.Count; isleId++)
{
MapRegion region = regions[isleId];
double[][] tileLocations = new double[region.turf.Count][];
for (int i = 0; i < tileLocations.Length; i++)
{
tileLocations[i] = new double[3];
TerrainVertData vertData = vertDatabase.GetVertDataFromRegionTile(region.turf[i], isleId);
if (vertData != null)
{
tileLocations[i][0] = region.turf[i].x;
tileLocations[i][1] = vertData.inlandPosition;
tileLocations[i][2] = region.turf[i].y;
}
else
{
LoggerTool.Post("Null from VertDB for " + region.turf[i].ToString());
}
}
int k = InitializeNumOfK(tileLocations.Length);
k = Mathf.Max(1, k);
Debug.Log(k + " centroid(s)");
KMedoids kmedoidsPam = new KMedoids(k);
kmedoidsPam.MaxIterations = 100;
KMedoidsClusterCollection <double> clusters = kmedoidsPam.Learn(tileLocations);
int[] labels = clusters.Decide(tileLocations);
Debug.Log("Number of labels = " + labels.Length);
for (int i = 0; i < labels.Length; i++)
{
points[(int)tileLocations[i][0], (int)tileLocations[i][2]].areaValue = regionId + labels[i];
}
regionId += k;
}
return(regionId);
}
public void doc_learn()
{
#region doc_learn
Accord.Math.Random.Generator.Seed = 0;
// Declare some observations
int[][] observations = new int[][]
{
new[] { 2, 6 }, // a
new[] { 3, 4 }, // b
new[] { 3, 8 }, // a
new[] { 4, 7 }, // a
new[] { 6, 2 }, // b
new[] { 6, 4 }, // b
new[] { 7, 3 }, // b
new[] { 7, 4 }, // b
new[] { 8, 5 }, // b
new[] { 7, 6 } // b
};
// Create a new 2-Medoids algorithm.
var kmedoidsPam = new KMedoids <int>(2, new Manhattan());
kmedoidsPam.MaxIterations = 100;
// Compute and retrieve the data centroids
var clusters = kmedoidsPam.Learn(observations);
// Use the centroids to parition all the data
int[] labels = clusters.Decide(observations);
#endregion
Assert.AreEqual(labels[0], labels[2]);
Assert.AreEqual(labels[0], labels[3]);
Assert.AreEqual(labels[4], labels[1]);
Assert.AreEqual(labels[4], labels[5]);
Assert.AreEqual(labels[4], labels[6]);
Assert.AreEqual(labels[4], labels[7]);
Assert.AreEqual(labels[4], labels[8]);
Assert.AreEqual(labels[4], labels[9]);
}
// Called by LandMap.GetZones (), returns number of subregions
public int ClusterLocationsAccordKMedoidsPAM(MapPoint[,] points)
{
// K-medoids cluster algorithm to separate locations in the regions
int regionId = 0;
for (int isleId = 0; isleId < regions.Count; isleId++)
{
MapRegion region = regions[isleId];
double[][] tileLocations = new double[region.turf.Count][];
for (int i = 0; i < tileLocations.Length; i++)
{
tileLocations[i] = new double[2];
tileLocations[i][0] = region.turf[i].x;
tileLocations[i][1] = region.turf[i].y;
}
int k = InitializeNumOfK(region.turf.Count);
Debug.Log(k + " centroid(s)");
KMedoids kmedoidsPam = new KMedoids(k);
kmedoidsPam.MaxIterations = 100;
KMedoidsClusterCollection <double> clusters = kmedoidsPam.Learn(tileLocations);
int[] labels = clusters.Decide(tileLocations);
Debug.Log("Number of labels = " + labels.Length);
for (int i = 0; i < labels.Length; i++)
{
points[(int)tileLocations[i][0], (int)tileLocations[i][1]].areaValue = regionId + labels[i];
}
regionId += k;
}
return(regionId);
}
public JsonResult PredictPossibleProducts()
{
var userId = 0;
int knnNum = 5;
int clusterNum = 4;
var userIdString = "";
if (HttpContext.Session["userid"] == null)
{
return(Json(new { errorCode = 1, errorMessage = "יוזר לא חוקי" }));
}
userIdString = HttpContext.Session["userid"].ToString();
var didParsed = Int32.TryParse(userIdString, out userId);
if (!didParsed)
{
return(Json(new { errorCode = 1, errorMessage = "יוזר לא חוקי" }));
}
var userGender = _context.Users
.Where(x => x.Id == userId)
.Select(x => x.Gender)
.SingleOrDefault();
var trainData = _context.Purchases
.OrderBy(x => x.UserId)
.Where(x => x.Product != null)
.Select(x => new
{
userId = x.UserId.Value,
size = x.Product.Size,
type = x.Product.ProductTypeId,
gender = x.Product.ProductType.Gender,
genderUser = x.User.Gender
})
.ToList();
if (trainData.Count < knnNum || trainData.Count < clusterNum)
{
return(Json(new { errorCode = 2, errorMessage = "אין מספיק מידע" }));
}
var inputs = trainData.Select(x =>
{
double[] res = new double[]
{
Convert.ToInt32(x.gender),
Convert.ToInt32(x.genderUser),
x.type.Value,
x.size
};
return(res);
})
.ToArray();
var codification = new Codification <double>()
{
CodificationVariable.Categorical,
CodificationVariable.Categorical,
CodificationVariable.Categorical,
CodificationVariable.Discrete
};
// Learn the codification from observations
var model = codification.Learn(inputs);
// Transform the mixed observations into only continuous:
double[][] newInputs = model.ToDouble().Transform(inputs);
KMedoids kmeans = new KMedoids(k: clusterNum);
var clusters = kmeans.Learn(newInputs);
int[] labels = clusters.Decide(newInputs);
var knn5 = new KNearestNeighbors(k: knnNum);
knn5.Learn(newInputs, labels);
var purchasesById = _context.Purchases
.Where(x => x.Product != null)
.Select(x => new
{
userId = x.UserId.Value,
size = x.Product.Size,
type = x.Product.ProductTypeId,
gender = x.Product.ProductType.Gender,
genderUser = x.User.Gender
})
.GroupBy(x => x.userId)
.ToList();
IList <Tuple <int, int[]> > labelsForUsers = new List <Tuple <int, int[]> >();
for (int i = 0; i < purchasesById.Count; i++)
{
var userInputs = purchasesById[i].
Select(x =>
{
double[] res = new double[]
{
Convert.ToInt32(x.gender),
Convert.ToInt32(x.genderUser),
x.type.Value,
x.size
};
return(res);
})
.ToArray();
double[][] newUserInputs = model.ToDouble().Transform(userInputs);
labelsForUsers.Add(new Tuple <int, int[]>(purchasesById[i].Key, clusters.Decide(newUserInputs).Distinct().ToArray()));
}
var productIdsUserBought = _context.Purchases
.Where(x => x.UserId == userId)
.Select(x => x.ProductId)
.Distinct()
.ToList();
var validProductTypeIds = _context.Purchases
.Where(x => x.UserId == userId)
.Select(x => x.Product.ProductTypeId)
.Distinct()
.ToList();
var productsToPredict = _context.Products
.Where(x => !productIdsUserBought.Contains(x.Id))
.Where(x => validProductTypeIds.Contains(x.ProductTypeId))
.Select(x => new
{
id = x.Id,
size = x.Size,
type = x.ProductTypeId,
gender = x.ProductType.Gender,
genderUser = userGender
})
.ToList();
var predInputs = productsToPredict.Select(x =>
{
double[] res = new double[]
{
Convert.ToInt32(x.gender),
Convert.ToInt32(x.genderUser),
x.type.Value,
x.size
};
return(res);
})
.ToArray();
double[][] newPredInputs = model.ToDouble().Transform(predInputs);
int[] newLabels = knn5.Decide(newPredInputs);
IList <int> productIdsPrediction = new List <int>();
var userLabels = labelsForUsers.Where(x => x.Item1 == userId).FirstOrDefault() != null?
labelsForUsers.Where(x => x.Item1 == userId).FirstOrDefault().Item2 : new int[0];
for (int i = 0; i < newLabels.Length; i++)
{
if (userLabels.Contains(newLabels[i]))
{
productIdsPrediction.Add(productsToPredict[i].id);
}
}
var predictedProduct = _context.Products
.Where(x => productIdsPrediction.Contains(x.Id))
.Select(x => new
{
Id = x.Id,
Name = x.Name,
Price = x.Price,
Size = x.Size,
PictureName = x.PictureName
})
.ToList();
return(Json(new { products = predictedProduct }, JsonRequestBehavior.AllowGet));
}
public ActionResult SuggestedItems()
{
var userId = HttpContext.Session["UserID"];
if (userId != null)
{
int id = (int)userId;
var orders = db.Orders.Where(x => x.UserID == id).Select(x => x.ID).ToList();
if (orders.Count != 0)
{
var joinDB = db.Orders.Join(db.Items,
o => o.ItemID,
i => i.ItemID,
(o, i) => new
{
isdeleted = i.IsDeleted,
UserID = o.UserID,
Gender = i.Gender,
Brand = i.Brand,
ItemTypeId = i.ItemTypeId
}).Where(x => x.isdeleted == false).ToList();
var trainData = joinDB.Select(X =>
new
{
UserID = X.UserID,
Gender = X.Gender,
Brand = X.Brand,
ItemTypeId = X.ItemTypeId
}).OrderBy(x => x.UserID).ToList();
DataTable table = new DataTable("");
table.Columns.Add("ItemTypeId", typeof(int));
table.Columns.Add("Brand", typeof(string));
table.Columns.Add("Gender", typeof(string));
foreach (var row in trainData)
{
table.Rows.Add(row.ItemTypeId, row.Brand, row.Gender);
}
var codebook = new Codification(table);
DataTable result = codebook.Apply(table);
// The resulting table can be transformed to jagged array:
double[][] matrix = Matrix.ToJagged(result);
KMedoids kmeans = new KMedoids(k: 6)
{
};
var clusters = kmeans.Learn(matrix);
int[] labels = clusters.Decide(matrix);
var purchasesById = joinDB.Select(X =>
new
{
UserID = X.UserID,
Gender = X.Gender,
Brand = X.Brand,
ItemTypeId = X.ItemTypeId
}).GroupBy(x => x.UserID).ToList();
IList <Tuple <int, int[]> > labelsForUsers = new List <Tuple <int, int[]> >();
// מתאים כל רכישה למבנה למידה
for (int i = 0; i < purchasesById.Count; i++)
{
table.Clear();
foreach (var row in purchasesById[i])
{
table.Rows.Add(row.ItemTypeId, row.Brand, row.Gender);
}
codebook = new Codification(table);
result = codebook.Apply(table);
// The resulting table can be transformed to jagged array:
double[][] newUserInputs = Matrix.ToJagged(result);
//var Currentlusters = kmeans.Learn(newUserInputs);
int[] currentLabels = clusters.Decide(newUserInputs).Distinct();
// יקח את כל הרכישות של יוזר שנמצאות בלאסטר
labelsForUsers.Add(new Tuple <int, int[]>(purchasesById[i].Key, currentLabels));
}
var itemIdsUserBought = db.Orders
.Where(x => x.UserID == id)
.Select(x => x.ItemID)
.Distinct()
.ToList();
var productsToPredict = db.Items
.Where(x => !itemIdsUserBought.Contains(x.ItemID))
.Where(x => x.IsDeleted == false)
.Select(x => new
{
id = x.ItemID,
Brand = x.Brand,
Gender = x.Gender,
ItemTypeId = x.ItemTypeId
}).ToList();
table.Clear();
foreach (var row in productsToPredict)
{
table.Rows.Add(row.ItemTypeId, row.Brand, row.Gender);
}
codebook = new Codification(table);
DataTable ItemInputs = codebook.Apply(table);
// The resulting table can be transformed to jagged array:
double[][] NewItemInputs = Matrix.ToJagged(ItemInputs);
int[] lol = clusters.Decide(NewItemInputs);
IList <int> productIdsPrediction = new List <int>();
var userLabels = labelsForUsers.Where(x => x.Item1 == id).FirstOrDefault().Item2;
for (int i = 0; i < lol.Length; i++)
{
if (userLabels.Contains(lol[i]))
{
productIdsPrediction.Add(productsToPredict[i].id);
}
}
var predictedProduct = db.Items
.Where(x => productIdsPrediction.Contains(x.ItemID))
.ToList();
return(View("SuggestedItems", predictedProduct));
}
}
return(View("SuggestedItems", new List <Item>()));
}
