def __init__(self, record_generator, model_drifter, nodes=1): self.record_generator = record_generator self.model_drifter = model_drifter self.model = model_drifter.construct_init_model() self.examples_per_round = nodes self.examples_in_current_round = 0 InputStream.__init__(self, self.getIdentifier(), nodes)
def __init__(self, identifier, nodes, dataset, label, targetStock='NASDAQ:GOOG', repetitions=0, repetition_interval=7, delay=False, prediction_index=0): self.prediction_index = prediction_index if isinstance(targetStock, list): self.m_stTargetStock = targetStock.pop(0) self.m_aTargetStocks = targetStock else: self.m_stTargetStock = targetStock self.configureFiles(dataset, label) self.readPrices() self.readAnnotations() self.preprocessAvgPrices() self.m_oCorrel = correlation(self.m_aPrices, [self.m_stTargetStock]) self.calcFeatureNames() self.parameters = "Financial(%s)" % dataset self.m_bDelay = False self.m_iRepetitions = repetitions self.m_iRepetitionInterval = repetition_interval self.m_iCurrRepitions = 0 InputStream.__init__(self, identifier, nodes)
def __init__(self, nodes, prediction_index=1, bPreprocess=False, numberOfStocks=None): self.m_lFeatures = [] self.m_lLabels = [] self.m_aDates = [] self.m_aStocks = [] self.m_lAvgPrices11 = cl.defaultdict(list) self.m_lAvgPrices50 = cl.defaultdict(list) self.m_lAvgPrices200 = cl.defaultdict(list) self.m_hPrices = cl.defaultdict(list) self.m_bPreprocess = bPreprocess self.prediction_index = prediction_index self.configureFiles() self.readPrices(numberOfStocks) if self.m_bPreprocess: self.preprocessFeatures() self.m_hPrices.clear() #clear off some memory self.m_lAvgPrices11.clear() self.m_lAvgPrices50.clear() self.m_lAvgPrices200.clear() else: self.m_stActStock = self.m_hPrices.keys()[0] InputStream.__init__(self, self.getIdentifier, nodes)
def __init__(self, dim, drift_prob,num_nodes): InputStream.__init__(self, "HiddenLayer" + "(" + str(dim) + ")",num_nodes) self.label_distribution = bernoulli(0.5) self.dim = dim self.drift_prob = drift_prob self.hidden_layer_size = int(log(dim, 2)) self.set_random_parameters() self.examples_in_current_macro_round=0
def __init__(self, file_name, target_attribute_name, num_nodes=1, positive_target_value=None, nominal_attributes_default_value=1.0): InputStream.__init__(self, file_name, num_nodes) uci_dataset_path = _get_path_to_uci_data_set(file_name) self.file_name = file_name self.target_attribute_name = target_attribute_name self.positive_target_value = positive_target_value self.nominal_attributes_default_value = nominal_attributes_default_value self._arff_description = describe(uci_dataset_path) self.data = self._read_data(uci_dataset_path, self._arff_description) self.arff_stream = open_stream(uci_dataset_path, self._arff_description) self._number_of_generated_examples = 0
def __init__(self, file_name, target_attribute_name, num_nodes=1, positive_target_value=None, nominal_attributes_default_value=1.0, iterations=1): InputStream.__init__(self, file_name, num_nodes) uci_dataset_path = _get_path_to_uci_data_set(file_name) # self.identifier = self._generatate_identifier(file_name) self.file_name = file_name self.target_attribute_name = target_attribute_name self.positive_target_value = positive_target_value self.nominal_attributes_default_value = nominal_attributes_default_value self._arff_description = describe(uci_dataset_path) self.arff_stream = open_stream(uci_dataset_path, self._arff_description) self._generated_examples = 0 self.iterations = iterations self.current_iteration = 1
def __init__(self, nodes, driftProb=0.028, prediction_index=1, numberOfStocks=None, normalize=False): self.m_lFeatures = [] self.m_aDates = [] self.m_aStocks = [] self.m_lAvgPrices11 = cl.defaultdict(list) self.m_lAvgPrices50 = cl.defaultdict(list) self.m_lAvgPrices200 = cl.defaultdict(list) self.m_hPrices = cl.defaultdict(list) self.bNormalize = normalize self.drift_prob = driftProb self.prediction_index = prediction_index self.configureFiles() self.readPrices(numberOfStocks) self.preprocessFeatures() self.m_lAvgPrices11.clear() self.m_lAvgPrices50.clear() self.m_lAvgPrices200.clear() self.m_stActStock = self.m_hPrices.keys()[0] InputStream.__init__(self, self.getIdentifier, nodes)
def __init__(self, record_generator, model, nodes=1): self.record_generator = record_generator self.model = model InputStream.__init__(self, self.getIdentifier(), nodes)
def __init__(self, data_file='higgs/higgs.svm', nodes = 1, repetitions = 1): InputStream.__init__(self,"libsvm_reader(data_file = %s)" % data_file, nodes) self.file_name = os.path.join(config.PATH_TO_DATASETS, data_file) self.labels = set([1,-1]) self.input_file_handle = open(self.file_name, 'r') self.current_line = self._fetch_line()