From 934c7441392f25077e71508e65b1cc58e043452b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= Date: Thu, 21 Feb 2019 12:00:09 -0300 Subject: [PATCH] Updating the documentation --- docs/build/doctrees/environment.pickle | Bin 2233540 -> 2275178 bytes docs/build/doctrees/modules.doctree | Bin 2573 -> 2573 bytes docs/build/doctrees/pyFTS.common.doctree | Bin 390620 -> 392100 bytes docs/build/doctrees/pyFTS.data.doctree | Bin 158707 -> 203917 bytes docs/build/doctrees/pyFTS.distributed.doctree | Bin 0 -> 32470 bytes docs/build/doctrees/pyFTS.doctree | Bin 4623 -> 4648 bytes docs/build/doctrees/pyFTS.hyperparam.doctree | Bin 22650 -> 23000 bytes .../doctrees/pyFTS.models.incremental.doctree | Bin 28398 -> 47190 bytes .../pyFTS.models.multivariate.doctree | Bin 113395 -> 119569 bytes .../doctrees/pyFTS.models.seasonal.doctree | Bin 149522 -> 155364 bytes docs/build/html/_modules/index.html | 4 +- .../_modules/pyFTS/benchmarks/Measures.html | 22 +- 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.../pyFTS/partitioners/Singleton.html | 6 +- .../pyFTS/partitioners/partitioner.html | 2 +- docs/build/html/_sources/modules.rst.txt | 2 +- docs/build/html/_sources/pyFTS.data.rst.txt | 16 +- .../html/_sources/pyFTS.distributed.rst.txt | 32 ++ .../html/_sources/pyFTS.hyperparam.rst.txt | 8 + .../_sources/pyFTS.models.incremental.rst.txt | 17 +- docs/build/html/_sources/pyFTS.rst.txt | 1 + docs/build/html/genindex.html | 162 +++++-- docs/build/html/modules.html | 20 +- docs/build/html/objects.inv | Bin 6122 -> 6461 bytes docs/build/html/py-modindex.html | 17 +- docs/build/html/pyFTS.common.html | 11 +- docs/build/html/pyFTS.data.html | 347 +++++++++++--- docs/build/html/pyFTS.distributed.html | 283 ++++++++++++ docs/build/html/pyFTS.html | 13 +- docs/build/html/pyFTS.hyperparam.html | 14 +- docs/build/html/pyFTS.models.html | 3 +- docs/build/html/pyFTS.models.incremental.html | 143 ++++-- .../build/html/pyFTS.models.multivariate.html | 28 +- docs/build/html/pyFTS.models.seasonal.html | 35 +- docs/build/html/searchindex.js | 2 +- docs/modules.rst | 2 +- docs/pyFTS.data.rst | 16 +- docs/pyFTS.distributed.rst | 32 ++ docs/pyFTS.hyperparam.rst | 8 + docs/pyFTS.models.incremental.rst | 17 +- docs/pyFTS.rst | 1 + pyFTS/data/artificial.py | 279 +++++------ 60 files changed, 2274 insertions(+), 403 deletions(-) create mode 100644 docs/build/doctrees/pyFTS.distributed.doctree create mode 100644 docs/build/html/_modules/pyFTS/distributed/spark.html create mode 100644 docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html rename docs/build/html/_modules/pyFTS/models/incremental/{Retrainer.html => TimeVariant.html} (94%) create mode 100644 docs/build/html/_sources/pyFTS.distributed.rst.txt create mode 100644 docs/build/html/pyFTS.distributed.html create mode 100644 docs/pyFTS.distributed.rst diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 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  • alpha_cut (pyFTS.common.fts.FTS attribute) + +
  • analytic_tabular_dataframe() (in module pyFTS.benchmarks.Util)
  • analytical_data_columns() (in module pyFTS.benchmarks.Util) @@ -235,7 +239,7 @@
  • auto_update (pyFTS.common.fts.FTS attribute)
  • averageloglikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method) @@ -248,8 +252,12 @@ + - - + - +
  • generate_lhs_flrs() (pyFTS.models.multivariate.mvfts.MVFTS method) +
  • +
  • generate_linear_periodic_gaussian() (in module pyFTS.data.artificial)
  • generate_non_recurrent_flrs() (in module pyFTS.common.FLR)
  • generate_recurrent_flrs() (in module pyFTS.common.FLR) +
  • +
  • generate_sinoidal_periodic_gaussian() (in module pyFTS.data.artificial)
  • generate_uniform_linear() (in module pyFTS.data.artificial)
  • @@ -793,6 +825,8 @@
  • get_benchmark_point_methods() (in module pyFTS.benchmarks.benchmarks)
  • get_benchmark_probabilistic_methods() (in module pyFTS.benchmarks.benchmarks) +
  • +
  • get_clustered_partitioner() (in module pyFTS.distributed.spark)
  • get_data() (in module pyFTS.data.AirPassengers) @@ -989,6 +1023,8 @@
  • get_models_forecasts() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)
  • get_name() (pyFTS.partitioners.partitioner.Partitioner method) +
  • +
  • get_partitioner() (in module pyFTS.distributed.spark)
  • get_point() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)
  • @@ -1040,6 +1076,8 @@
  • (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)
  • +
  • get_variables() (in module pyFTS.distributed.spark) +
  • getChildren() (pyFTS.common.tree.FLRGTreeNode method)
  • getStr() (pyFTS.common.tree.FLRGTreeNode method) @@ -1055,6 +1093,8 @@ + -
    @@ -1233,7 +1277,11 @@
  • mape_interval() (in module pyFTS.benchmarks.Measures)
  • mask (pyFTS.models.multivariate.variable.Variable attribute) + +
  • max_lag (pyFTS.common.fts.FTS attribute)
  • membership() (in module pyFTS.partitioners.FCM) @@ -1251,11 +1299,11 @@
  • membership_function (pyFTS.partitioners.partitioner.Partitioner attribute)
  • merge() (pyFTS.common.fts.FTS method) -
  • -
  • mf (pyFTS.common.FuzzySet.FuzzySet attribute)
  • set_ordered() (in module pyFTS.common.FuzzySet)
  • - - +
  • Transformation (class in pyFTS.common.Transformations)
  • -
  • transformation (pyFTS.partitioners.partitioner.Partitioner attribute) +
  • transformation (pyFTS.models.multivariate.variable.Variable attribute) + +
  • transformations (pyFTS.common.fts.FTS attribute)
  • transformations_param (pyFTS.common.fts.FTS attribute) @@ -2074,8 +2172,12 @@
  • window_index() (in module pyFTS.models.nonstationary.common)
  • -
  • window_length (pyFTS.models.incremental.Retrainer.Retrainer attribute) +
  • window_length (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute) + +
  • winkler_mean() (in module pyFTS.benchmarks.Measures)
  • winkler_score() (in module pyFTS.benchmarks.Measures) diff --git a/docs/build/html/modules.html b/docs/build/html/modules.html index e9dbb87..3b22708 100644 --- a/docs/build/html/modules.html +++ b/docs/build/html/modules.html @@ -130,6 +130,7 @@
  • Submodules
  • pyFTS.data.common module
  • Datasets
  • +
  • Artificial and synthetic data generators
  • AirPassengers dataset
  • Bitcoin dataset
  • DowJones dataset
  • @@ -144,7 +145,6 @@
  • SONDA dataset
  • S&P 500 dataset
  • TAIEX dataset
  • -
  • pyFTS.data.artificial module
  • Henon chaotic time series
  • Logistic_map chaotic time series
  • Lorentz chaotic time series
  • @@ -153,16 +153,31 @@
  • Sunspots dataset
  • +
  • pyFTS.distributed package +
  • pyFTS.hyperparam package
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    A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False

    +
    +
    +is_wrapper = None¶
    +

    Indicates that this model is a wrapper for other(s) method(s)

    +
    +
    lags = None¶
    @@ -1907,8 +1913,9 @@ needed to forecast a single step ahead

  • nodes – a list with the dispy cluster nodes addresses
  • explain – try to explain, step by step, the one-step-ahead point forecasting result given the input data.
  • generators – for multivariate methods on multi step ahead forecasting, generators is a dict where the keys -are the variables names (except the target_variable) and the values are lambda functions that -accept one value (the actual value of the variable) and return the next value.
  • +are the dataframe columun names (except the target_variable) and the values are lambda functions that +accept one value (the actual value of the variable) and return the next value or trained FTS +models that accept the actual values and forecast new ones.
  • diff --git a/docs/build/html/pyFTS.data.html b/docs/build/html/pyFTS.data.html index 59b25f2..c6fde7a 100644 --- a/docs/build/html/pyFTS.data.html +++ b/docs/build/html/pyFTS.data.html @@ -28,7 +28,7 @@ - + + + + + +
    +
    +
    +
    + +
    +

    pyFTS.distributed package¶

    +
    +

    Module contents¶

    +
    +
    +

    Submodules¶

    +
    +
    +

    pyFTS.distributed.dispy module¶

    +
    +
    +

    pyFTS.distributed.spark module¶

    +
    +
    +pyFTS.distributed.spark.create_multivariate_model(**parameters)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.create_spark_conf(**kwargs)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.create_univariate_model(**parameters)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.distributed_predict(data, model, **kwargs)[source]¶
    +
    +++ + + + + + +
    Parameters:
      +
    • model –
    • +
    • data –
    • +
    • url –
    • +
    • app –
    • +
    +
    Returns:

    +
    +
    + +
    +
    +pyFTS.distributed.spark.distributed_train(model, data, **kwargs)[source]¶
    +
    +++ + + + + + +
    Parameters:
      +
    • model –
    • +
    • data –
    • +
    • url –
    • +
    • app –
    • +
    +
    Returns:

    +
    +
    + +
    +
    +pyFTS.distributed.spark.get_clustered_partitioner(explanatory_variables, target_variable, **parameters)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.get_partitioner(shared_partitioner, type='common', variables=[])[source]¶
    +
    +++ + + + + + +
    Parameters:part –
    Returns:
    +
    + +
    +
    +pyFTS.distributed.spark.get_variables(**parameters)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.share_parameters(model, context, data)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.slave_forecast_multivariate(data, **parameters)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.slave_forecast_univariate(data, **parameters)[source]¶
    +
    +++ + + + + + +
    Parameters:data –
    Returns:
    +
    + +
    +
    +pyFTS.distributed.spark.slave_train_multivariate(data, **parameters)[source]¶
    +
    + +
    +
    +pyFTS.distributed.spark.slave_train_univariate(data, **parameters)[source]¶
    +
    +++ + + + + + +
    Parameters:data –
    Returns:
    +
    + +
    +
    + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/pyFTS.html b/docs/build/html/pyFTS.html index 89ede86..162aee5 100644 --- a/docs/build/html/pyFTS.html +++ b/docs/build/html/pyFTS.html @@ -142,6 +142,7 @@
  • Submodules
  • pyFTS.data.common module
  • Datasets
  • +
  • Artificial and synthetic data generators
  • AirPassengers dataset
  • Bitcoin dataset
  • DowJones dataset
  • @@ -156,7 +157,6 @@
  • SONDA dataset
  • S&P 500 dataset
  • TAIEX dataset
  • -
  • pyFTS.data.artificial module
  • Henon chaotic time series
  • Logistic_map chaotic time series
  • Lorentz chaotic time series
  • @@ -165,11 +165,19 @@
  • Sunspots dataset
  • +
  • pyFTS.distributed package +
  • pyFTS.hyperparam package
  • pyFTS.models package
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