IndexingSignalAggregatorRunningMeanAndVarianceInternalState

AI Overview😉

  • The potential purpose of this module is to calculate the mean and variance of a dataset in a numerically stable way, which is useful for aggregating and analyzing signals or data points in the context of search indexing. This allows Google to process and make sense of large amounts of data in an efficient and accurate manner.
  • This module could impact search results by providing a more accurate and efficient way to process and analyze data, which could lead to more relevant and personalized search results. For example, it could be used to calculate the average rating of a product or the frequency of certain keywords on a webpage, which could then be used to rank search results.
  • A website may change things to be more favorable for this function by providing high-quality, structured data that can be easily processed and analyzed by the algorithm. This could include providing accurate and consistent metadata, such as ratings or reviews, and using standardized schema markup to help Google understand the content and context of the webpage. Additionally, websites could focus on creating high-quality, relevant, and informative content that is likely to be useful to users, which could lead to higher rankings and more visibility in search results.

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GoogleApi.ContentWarehouse.V1.Model.IndexingSignalAggregatorRunningMeanAndVarianceInternalState (google_api_content_warehouse v0.4.0)

Internal state of the West & Chan running variance algorithm. Fields of this proto should not be accessed directly; instead, please use RunningMeanAndVarianceUtil. The fields of this message only have meaning in the context of the West & Chan algorithm, which is documented (or Wikipedia-linked) in the doc comments of RunningMeanAndVarianceUtil. We do however give some explanation of the meanings of these fields in the context of the algorithm (i.e. if you have the Wikipedia page open and are ready to do some math). Also see the file doc of RunningMeanAndVarianceUtil for a specification and more info about the algorithm. Notation: The data set is X = {(x_1, w_1), ..., (x_n, w_n)}. It consists of n weighted data points. The ith data point has value x_i and weight w_i. REQUIRES: x_i is finite for each i. w_i is finite for each i. w_i >= 0 for each i.

Attributes

  • m2 (type: float(), default: nil) - The variable which in the Wikipedia page is referred to as M_2: m2 = w_1 (x_1 - mean)^2 + ... + w_n (x_n - mean)^2. The algorithm implemented in RunningMeanAndVarianceUtil provides a way to update m2 in a numerically stable way when the data set grows. If total_weight = 0, then m2 is meaningless, and its value is unspecified, except that it must be finite and >= 0.
  • mean (type: float(), default: nil) - Mean of the data set, mean = (w_1 x_1 + ... + w_n x_n) / total_weight. The algorithm implemented in RunningMeanAndVarianceUtil provides a way to update this mean in a numerically stable way when the data set grows. If total_weight = 0, then mean is meaningless, and its value is unspecified, except that it must be finite.
  • totalWeight (type: float(), default: nil) - Total weight of the data set, total_weight = w_1 + ... + w_n.

Summary

Types

t()

Functions

decode(value, options)

Unwrap a decoded JSON object into its complex fields.

Types

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t()

@type t() ::
  %GoogleApi.ContentWarehouse.V1.Model.IndexingSignalAggregatorRunningMeanAndVarianceInternalState{
    m2: float() | nil,
    mean: float() | nil,
    totalWeight: float() | nil
  }

Functions

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decode(value, options)

@spec decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.