def kmeans(x,validation_x=None,**kwargs): """ Build a KMeans model (kwargs are the same arguments that you can find in FLOW) :return: A new clustering model """ return h2o_model_builder.unsupervised_model_build(x,validation_x,"kmeans",kwargs)
def kmeans(x,validation_x=None,**kwargs): """ Build a KMeans model (kwargs are the same arguments that you can find in FLOW) :return: A new clustering model """ return h2o_model_builder.unsupervised_model_build(x,validation_x,"kmeans",kwargs)
def autoencoder(x,**kwargs): """ Build an Autoencoder :param x: Columns with which to build an autoencoder :param kwargs: Additional arguments to pass to the autoencoder. :return: A new autoencoder model """ return h2o_model_builder.unsupervised_model_build(x,None,"autoencoder",kwargs)
def autoencoder(x,**kwargs): """ Build an Autoencoder :param x: Columns with which to build an autoencoder :param kwargs: Additional arguments to pass to the autoencoder. :return: A new autoencoder model """ return h2o_model_builder.unsupervised_model_build(x,None,"autoencoder",kwargs)
def svd(x,validation_x=None,**kwargs): """ Singular value decomposition of a H2O dataset using the power method. :param nv: The number of right singular vectors to be computed. This must be between 1 and min(ncol(training_frame), nrow(training_frame)) inclusive. :param max_iterations: The maximum number of iterations to run each power iteration loop. Must be between 1 and 1e6 inclusive.max_iterations The maximum number of iterations to run each power iteration loop. Must be between 1 and 1e6 inclusive. :param transform: A character string that indicates how the training data should be transformed before running PCA. Possible values are "NONE": for no transformation, "DEMEAN": for subtracting the mean of each column, "DESCALE": for dividing by the standard deviation of each column, "STANDARDIZE": for demeaning and descaling, and "NORMALIZE": for demeaning and dividing each column by its range (max - min). :param seed: (Optional) Random seed used to initialize the right singular vectors at the beginning of each power method iteration. :param use_all_factor_levels: (Optional) A logical value indicating whether all factor levels should be included in each categorical column expansion. If FALSE, the indicator column corresponding to the first factor level of every categorical variable will be dropped. Defaults to TRUE. :return: a new dim reduction model """ return h2o_model_builder.unsupervised_model_build(x,validation_x,"svd",kwargs)
def svd(x,validation_x=None,**kwargs): """ Singular value decomposition of a H2O dataset using the power method. :param nv: The number of right singular vectors to be computed. This must be between 1 and min(ncol(training_frame), nrow(training_frame)) inclusive. :param max_iterations: The maximum number of iterations to run each power iteration loop. Must be between 1 and 1e6 inclusive.max_iterations The maximum number of iterations to run each power iteration loop. Must be between 1 and 1e6 inclusive. :param transform: A character string that indicates how the training data should be transformed before running PCA. Possible values are "NONE": for no transformation, "DEMEAN": for subtracting the mean of each column, "DESCALE": for dividing by the standard deviation of each column, "STANDARDIZE": for demeaning and descaling, and "NORMALIZE": for demeaning and dividing each column by its range (max - min). :param seed: (Optional) Random seed used to initialize the right singular vectors at the beginning of each power method iteration. :param use_all_factor_levels: (Optional) A logical value indicating whether all factor levels should be included in each categorical column expansion. If FALSE, the indicator column corresponding to the first factor level of every categorical variable will be dropped. Defaults to TRUE. :return: a new dim reduction model """ kwargs['_rest_version'] = 99 return h2o_model_builder.unsupervised_model_build(x,validation_x,"svd",kwargs)