def __init__(self, k=10, n=100, m=50): Data.__init__(self) self.k = k self.m = m self.means = rnd.uniform(mean_low, mean_high, (self.k, self.m)) self.divs = rnd.uniform(div_low, div_high, (self.k, self.m)) self.data, self.labels = self.generate_data(n)
def __init__2(self): import numpy as np iris = load_iris() feature_vector = iris.data.tolist() new_column1 = (len(feature_vector) / 2) * ["a"] + (len(feature_vector) / 2) * ["b"] new_column2 = (len(feature_vector) / 2) * ["c"] + (len(feature_vector) / 2) * ["d"] feature_vector = [[new_column2[i]] + feature_vector[i] + [new_column1[i]] for i in range(len(feature_vector))] feature_vector = np.array(feature_vector, dtype=object) target_vector = [iris.target_names[i] for i in iris.target] feature_names = iris.feature_names feature_names.append("test") feature_names.append("test2") Data.__init__(self, feature_vector, target_vector, feature_names, name="Iris")
def __init__(self, headers, pdata, evals, evecs, means, filename=None): Data.__init__(self) self.eigenValues = evals self.eigenVectors = evecs self.dataMeans = means self.dataHeaders = headers self.matrix_data = pdata # add to raw_headers and raw data types for idx in range(len(headers)): if idx > 9: self.raw_headers.append("P%d" % (idx)) else: self.raw_headers.append("P0%d" % (idx)) self.raw_types.append("numeric") # print self.raw_headers # add a numpy array to raw_data self.raw_data = np.squeeze(np.asarray(pdata)) # add to header2raw and header2matrix for index in range(len(self.raw_headers)): self.header2raw[self.raw_headers[index]] = index self.header2matrix[self.raw_headers[index]] = index
def __init__(self, ogDataHeaders, projectedData, eigenValues, eigenVectors, ogDataMeans): Data.__init__(self) self.eigenValues = eigenValues self.eigenVectors = eigenVectors self.meanDataValues = ogDataMeans self.projectedHeaders = ogDataHeaders self.matrix = projectedData self.rawHeaders = ["P"+`i` for i in range(len(ogDataHeaders))]
def __init__(self): wine = load_wine() feature_vector = wine.data target_vector = [wine.target_names[i] for i in wine.target] feature_names = wine.feature_names Data.__init__(self, feature_vector, target_vector, feature_names, name="Wine")
def __init__(self, projected_data, eigenvectors, eigenvalues, original_means, data_headers): Data.__init__(self) self.data = projected_data self.eigenvalues = eigenvalues self.eigenvectors = eigenvectors self.original_means = original_means self.original_headers = data_headers self.organize_stuff()
def __init__(self, args): self.args = args Data.__init__(self, self.args.input_file) self.population = [] for _ in range(self.args.population_size): self.population.append(Individual(self.items, self.args)) self.elite = Elite(self.args, list(self.items.keys()), self.population) self.registry = Registry(self.elite) self.runEvolutive()
def __init__(self, m_file, **kwargs): self.surface = None Data.__init__(self, m_file, **kwargs) self.container = gwy.gwy_file_load(self.m_file, gwy.RUN_NONINTERACTIVE) gwy.gwy_app_data_browser_add(self.container) self.channel_id = gwy.gwy_app_data_browser_find_data_by_title( self.container, "*SE*" )[0] gwy.gwy_app_data_browser_select_data_field(self.container, self.channel_id) self.extract_meta(gwyddion.get_meta_ids(self.container)[0])
def __init__(self, m_file, **kwargs): self.electrolyte = None self.gas = None self.surface = None self.ph = None self.re = "" self.ce = None self.we = None self.ecell = None self.icell = None Data.__init__(self, m_file, **kwargs)
def __init__(self, data, headers, codebooks, codes, errors): Data.__init__(self) self.data = data self.headers = headers self.codebooks = codebooks self.codes = codes self.errors = errors self.k = self.codebooks.shape[0] self.N = self.codes.shape[0] self.organize_stuff()
def __init__(self): cancer = load_breast_cancer() feature_vector = cancer.data target_vector = [cancer.target_names[i] for i in cancer.target] feature_names = cancer.feature_names Data.__init__(self, feature_vector, target_vector, feature_names, name="BreastCancer")
def __init__(self): iris = load_iris() feature_vector = iris.data target_vector = [iris.target_names[i] for i in iris.target] feature_names = iris.feature_names Data.__init__(self, feature_vector, target_vector, feature_names, name="Iris")
def __init__(self): # Read data from file df = pd.read_csv("../data/bank_note/data_banknote_authentication.csv", header=None) # Add all columns as feature vector expect the target column feature_vector = df.iloc[:, :-1].values target_vector = df.iloc[:, -1].values # Set the column names as the feature names feature_names = ["variance of WTI", "skewness of WTI", "curtosis of Wavelet", "entropy of image"] Data.__init__(self, feature_vector, target_vector, feature_names, name="BankNote")
def __init__(self): feature_names = [ "Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Marital Status", "Occupation", "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss", "Hours per week", "Country" ] data = pd.read_csv('../data/adult/adult.data', na_values="NA", names=feature_names + ["Salary"], header=None).values labels = data[:, 14] Data.__init__(self, data[:, :-1], labels, feature_names, name="Adult")
def __init__(self, path): # Read data from file df = pd.read_csv(path) # Add all columns as feature vector expect the target vector feature_vector = df.iloc[:, 0:-1].values # Get the last column as target vector target_vector = df.iloc[:, -1].values # Set the column names as the feature names feature_names = list(df)[:-1] Data.__init__(self, feature_vector, target_vector, feature_names)
def __init__(self): print("Loading digits dataset!!!") digits_data = load_digits() feature_vector = digits_data.images.reshape(1797, 64) target_vector = digits_data.target feature_names = map(lambda index: str((index / 8, index % 8)), range(64)) Data.__init__(self, feature_vector, target_vector, feature_names, name="Digits") print("\tDone!!")
def __init__(self,base,quote): self.base = base self.quote = quote self.pair = self.base + '_' + self.quote self.price = Poloniex.getPrice(self,self.pair) Data.__init__(self) Charter.__init__(self) #Backtester.__init__(self) #xmr = FX("BTC","XMR") #xmr.saveChartData() #xmr.chartCandles('7D') #xmr.showChart()
def __init__(self): # Read data from file original_df = pd.read_csv("../data/housing/train.csv") # Get the target vector # Make the sale price discrete as ["low", "medium", "high"] target_vector = self.split(original_df["SalePrice"].values) df = original_df.drop(columns=["SalePrice"]) # Convert null values to string "NA" df.fillna("NA", inplace=True) # Merge two columns df["Bathroom Amount"] = df["BsmtFullBath"] + df["FullBath"] df["Toilet Amount"] = df["BsmtHalfBath"] + df["HalfBath"] # Remove not needed columns df.drop(columns=not_used_columns, inplace=True) # Change values of the columns to make them more readable for column_name in useful_categorical_columns.keys(): df[column_name] = df[column_name].map( categorical_values[column_name]) for column_name in categorical_to_numerical.keys(): df[column_name] = df[column_name].map( categorical_to_numerical[column_name]) # Convert to numerical values to int df[useful_numerical_columns.keys() + new_columns] = df[useful_numerical_columns.keys() + new_columns].astype(int) # Change column names to make them more readable df.rename(columns=useful_numerical_columns, inplace=True) df.rename(columns=useful_categorical_columns, inplace=True) feature_vector = df.values # Set the column names as the feature names feature_names = list(df) Data.__init__(self, feature_vector, target_vector, feature_names, name="Housing")
def __init__(self, m_file, **kwargs): self.surface = None self.tip = None Data.__init__(self, m_file, **kwargs) self.set_settings() self.container = gwy.gwy_file_load(self.m_file, gwy.RUN_NONINTERACTIVE) gwy.gwy_app_data_browser_add(self.container) self.img_topo_fwd = None self.img_topo_bwd = None self.topo_fwd_ch = self.return_topo_fwd_ch() self.topo_bwd_ch = self.return_topo_bwd_ch() self.size = None self.rotation = None self.line_time = None self.bias = None self.current = None self.xoffset = None self.yoffset = None self.scan_duration = None self.extract_meta(gwyddion.get_meta_ids(self.container)[0])
def __init__(self): # Read data from file df = pd.read_csv("../data/abalone/abalone.data", header=None) # Add all columns as feature vector expect the age and sex feature_vector = df.iloc[:, 1:-1].values # Make the sale price discrete as ["young","old"] target_vector = self.split(df.iloc[:, -1].values) # Set the column names as the feature names feature_names = [ "length", "diameter", "height", "whole weight", "shucked weight", "viscera weight", "shell weight" ] Data.__init__(self, feature_vector, target_vector, feature_names, name="Abalone")
def __init__(self, args): logging.info('==================> Started Stamp-Finder <==================') Data.__init__(self, args) # See if a quick run is wanted. if not self.quick: Q = raw_input('\nDo you want to skip Hashing for faster run? [yN] ') if Q == ('y' or 'Y'): self.quick = True self.create_files() if not self.quick: self.Main_hash() self.sfinder() if not self.quick: self.Main_hash(final=True) return
def __init__(self, filename, varname, levellist=None, **kwargs): """ Data4D class This class implements the functionality to generate Data4D objekt. EXAMPLES ======== """ self.levellist = {} if not levellist is None: level = 0 for k in levellist: self.levellist[int(k)] = level level += 1 self.data4D = [] Data.__init__(self, filename, varname, **kwargs) self.mulc(self.scale_factor, copy=False)
def __init__(self, args): logging.info( '==================> Started Stamp-Finder <==================') Data.__init__(self, args) # See if a quick run is wanted. if not self.quick: Q = raw_input( '\nDo you want to skip Hashing for faster run? [yN] ') if Q == ('y' or 'Y'): self.quick = True self.create_files() if not self.quick: self.Main_hash() self.sfinder() if not self.quick: self.Main_hash(final=True) return
def __init__(self): basedir = "../data/" feature_names = [ "weight", "food_time", "food_cho", "a1_start", "a1_duration_hrs", "a1_hrs_after_food", "a1_end", "a2_start", "a2_duration_hrs", "a2_hrs_after_food", "a2_end", "before_food_hyper", "before_food_hypo", "patient_adolescent", "patient_adult", "patient_child", "food_name_apple", "food_name_banana", "food_name_bread_milk", "food_name_cookie", "food_name_french_fries", "food_name_hamburger_meal", "food_name_juice_nuts", "food_name_milk_crackers", "food_name_peach", "food_name_rice_beans", "food_name_strawberries", "food_name_watermelon", "food_type_meal", "food_type_snack", "a1_type_0", "a1_type_activity", "a1_type_sports", "a1_name_0", "a1_name_bicycling_16kph", "a1_name_chores", "a1_name_dancing", "a1_name_mopping", "a1_name_mountain_climbing", "a1_name_running", "a1_name_swimming", "a1_name_walking", "a2_type_0", "a2_type_activity", "a2_type_sports", "a2_name_0", "a2_name_dancing", "a2_name_skating", "a2_name_walking" ] # list(genfromtxt(basedir + 'diabetes_model_feature_names.csv', delimiter=';')) data = genfromtxt(basedir + 'diabetes_model_x_train.csv', delimiter=';') labels = genfromtxt(basedir + 'diabetes_model_y_train.csv', delimiter=';') label_names = ['hyper', 'hypo', 'ok'] labels = [label_names[np.argmax(v)] for v in labels] # data = pd.read_csv('../data/diabetes_model_x_train.csv', na_values="NA", names=feature_names, # header=None).values # labels = pd.read_csv('../data/diabetes_model_x_train.csv', na_values="NA", names=['label'], # header=None).values Data.__init__(self, data, labels, feature_names, name="Diabetes")
def __init__(self, name=None, description='', kind=None, **kwargs): Data.__init__(self, name=name, description=description, type=kind) return
def __init__(self, username, password, url, description): self.username = username self.password = password self.url = url Data.__init__(self, description)
def __init__(self, path_to_data): start_epoch = 0 # seconds end_epoch = 1.25 # seconds sample_rate = 200 Data.__init__(self, path_to_data, start_epoch, end_epoch, sample_rate)
def __init__(self, m_file, **kwargs): Data.__init__(self, m_file, **kwargs)
def __init__(self, ratings=RATINGS, entity_names=MOVIE_NAMES, debug=False, load_previous=False): Data.__init__(self, ratings=ratings, entity_names=entity_names, debug=debug, load_previous=load_previous)
def __init__(self, dataset,features): Data.__init__(self) self.features = features self.dataset = dataset self._compute_features_dataset(dataset)
def __init__(self, x, y, test_ratio=0.1, val_ratio=0.0, k_fold=0, shuffle=True, threshold=0): Data.__init__(self, x, y, test_ratio, val_ratio, k_fold, shuffle) self.threshold = threshold self.vocabulary = {self.UNDEF_WORD: 0} self.preprocess()