Отказоустойчивая кластеризация в windows server

Содержание
  1. Модная одежда для подростков мальчиков 2020-2021
  2. Function List
  3. Finding Information about the Model
  4. Sample Query 1: Getting Model Metadata by Using DMX
  5. Sample Query 2: Retrieving Model Metadata from the Schema Rowset

Модная одежда для подростков мальчиков 2020-2021

Каждое из направлений поможет подчеркнуть индивидуальность личности, а правильно подобранная и стильная одежда для подростков мальчиков станет одним из факторов приобретения парнями особого внимания девочек, хорошей репутации среди одногодок, уважения у старших.

Красивая одежда для подростков парней 2020-2021 – это опять же джинсы разных видов и направлений, без которых современные тинэйджеры никуда.

В подростковый гардероб мальчика может входить модная одежда для подростков в виде брюк прямого кроя, стильные рубашки и футболки, которые можно сочетать, как с джинсами, так и брюками, а для холодных деньков пригодятся свитера, толстовки, бомберы, свитшоты и другие варианты кофт.

Как мальчики, так и девочки оценят такие стили одежды для подростков, как преппи стиль и хипстерский, ставшие трендовыми для разных категорий подростков в этом сезоне.

Хотя концепция названых стилей отличается друг от друга, оба стиля презентуют удобную и подчеркивающую индивидуальность молодых людей одежду.

Увидеть, какой будет модная одежда для подростков мальчиков, вы можете ниже.

Function List

All Microsoft algorithms support a common set of functions. However, models that are built by using the Microsoft Clustering algorithm support the additional functions that are listed in the following table.

Prediction Function Usage
Cluster (DMX) Returns the cluster that is most likely to contain the input case.
ClusterDistance (DMX) Returns the distance of the input case from the specified cluster, or if no cluster is specified, the distance of the input case from the most likely cluster. Returns the probability that the input case belongs to the specified cluster.
ClusterProbability (DMX) Returns the probability that the input case belongs to the specified cluster.
IsDescendant (DMX) Determines whether one node is a child of another node in the model.
IsInNode (DMX) Indicates whether the specified node contains the current case.
PredictAdjustedProbability (DMX) Returns the weighted probability.
PredictAssociation (DMX) Predicts membership in an associative dataset.
PredictCaseLikelihood (DMX) Returns the likelihood that an input case will fit in the existing model.
PredictHistogram (DMX) Returns a table of values related to the current predicted value.
PredictNodeId (DMX) Returns the Node_ID for each case.
PredictProbability (DMX) Returns probability for the predicted value.
PredictStdev (DMX) Returns the predicted standard deviation for the specified column.
PredictSupport (DMX) Returns the support value for a specified state.
PredictVariance (DMX) Returns the variance of a specified column.

For the syntax of specific functions, see Data Mining Extensions (DMX) Function Reference.

Finding Information about the Model

All mining models expose the content learned by the algorithm according to a standardized schema, the mining model schema rowset. You can create queries against the mining model schema rowset by using Data Mining Extension (DMX) statements. In SQL Server 2017, you can also query the schema rowsets directly as system tables.

Sample Query 1: Getting Model Metadata by Using DMX

The following query returns basic metadata about the clustering model, , that you created in the Basic Data Mining Tutorial. The metadata available in the parent node of a clustering model includes the name of the model, the database where the model is stored, and the number of child nodes in the model. This query uses a DMX content query to retrieve the metadata from the parent node of the model:

Note

You must enclose the name of the column, CHILDREN_CARDINALITY, in brackets to distinguish it from the Multidimensional Expressions (MDX) reserved keyword of the same name.

Example results:

MODEL_CATALOG TM_Clustering
MODEL_NAME Adventure Works DW
NODE_CAPTION Cluster Model
NODE_SUPPORT 12939
CHILDREN_CARDINALITY 10
NODE_DESCRIPTION All

For a definition of what these columns mean in a clustering model, see Mining Model Content for Clustering Models (Analysis Services – Data Mining).

Sample Query 2: Retrieving Model Metadata from the Schema Rowset

By querying the data mining schema rowset, you can find the same information that is returned in a DMX content query. However, the schema rowset provides some additional columns. These include the parameters that were used when the model was created, the date and time that the model was last processed, and the owner of the model.

The following example returns the date the model was created, modified, and last processed, together with the clustering parameters that were used to build the model, and the size of the training set. This information can be useful for documenting the model, or for determining which of the clustering options were used to create an existing model.

Example results:

MODEL_NAME TM_Clustering
DATE_CREATED 10/12/2007 7:42:51 PM
LAST_PROCESSED 10/12/2007 8:09:54 PM
PREDICTION_ENTITY Bike Buyer
MINING_PARAMETERS CLUSTER_COUNT=10, CLUSTER_SEED=0, CLUSTERING_METHOD=1, MAXIMUM_INPUT_ATTRIBUTES=255, MAXIMUM_STATES=100, MINIMUM_SUPPORT=1, MODELLING_CARDINALITY=10, SAMPLE_SIZE=50000, STOPPING_TOLERANCE=10
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