def __init__(self): SupervisedDataSet.__init__(self, 2, 1) with open('C:\Users\Brian\Desktop\Brian\Universitetet\Kandidat\Master Thesis\WeLoveGREEN-ENERGY\DATASET_FOR_GREEN_ENERGY_PLOTTING\WIND_TEMP_PRODUCTION_AVERAGE.csv', 'rb') as csvfile: dat = csv.reader(csvfile, delimiter=';') for row in dat: # print 'sample 0: ' + row[0] + ' sample 1: ' + row[1] self.addSample([int(row[1]),int(row[2])],[int(row[0])])
def __init__(self, filename=None): SupervisedDataSet.__init__(self,0,0) self.nCls = 0 self.nSamples = 0 self.classHist = {} self.filename = '' if filename is not None: self.loadData(filename)
def __init__(self, filename=None): SupervisedDataSet.__init__(self, 0, 0) self.nCls = 0 self.nSamples = 0 self.classHist = {} self.filename = '' if filename is not None: self.loadData(filename)
def _setDataFields( self, x, y ): if not len(x): raise Exception("no input data found") SupervisedDataSet.__init__( self, len(x[0]), 1 ) self.setField( 'input' , x ) self.setField( 'target' , y ) flat_labels = list( self.getField('target').flatten() ) classes = list(set( flat_labels )) self._classes = classes self.nClasses = len(classes) for class_ in classes: self.classHist[class_] = flat_labels.count(class_)
def _setDataFields(self, x, y): if not len(x): raise Exception("no input data found") SupervisedDataSet.__init__(self, len(x[0]), 1) self.setField('input', x) self.setField('target', y) flat_labels = list(self.getField('target').flatten()) classes = list(set(flat_labels)) self._classes = classes self.nClasses = len(classes) for class_ in classes: self.classHist[class_] = flat_labels.count(class_)
def __init__(self, oneInN=False): if oneInN: SupervisedDataSet.__init__(self, 2, 2) self.addSample([0, 0], [0, 1]) self.addSample([0, 1], [1, 0]) self.addSample([1, 0], [1, 0]) self.addSample([1, 1], [0, 1]) else: SupervisedDataSet.__init__(self, 2, 1) self.addSample([0, 0], [0]) self.addSample([0, 1], [1]) self.addSample([1, 0], [1]) self.addSample([1, 1], [0])
def __init__(self,begin=0,end=40000): SupervisedDataSet.__init__(self, 5, 1) rid_url = fileio.read_file_to_dict(FEATURE_PATH+"url_id_feature.dat") rid_sent = fileio.read_file_to_dict(FEATURE_PATH+"rid_sentratio.dict") rid_general = fileio.read_file_to_dict(FEATURE_PATH+"rid_general.dict") rid_len = fileio.read_file_to_dict(FEATURE_PATH+"rid_lenratio.dict") rid_cate = fileio.read_file_to_dict(FEATURE_PATH+"rid_cateratio.dict",delimiter=None) fake = fileio.read_file_to_list("data/target/all_replicaId.dat") for rid in rid_url.keys()[begin:end]: inps = [rid_url[rid],rid_sent[rid],rid_general[rid],rid_len[rid],rid_cate[rid]] target = [1 if rid in fake else 0] self.addSample(inps,target)
def __init__(self, number_of_days_before, quotes): SupervisedDataSet.__init__(self, number_of_days_before, 1) gains = [] for i, quote in enumerate(quotes): if i >= 1: gain = (quote - quotes[i-1])/quotes[i-1] gains.append(gain) for i, quote in enumerate(gains): if i >= number_of_days_before: first_day = i - number_of_days_before input = gains[first_day:i] output = [gains[i]] self.addSample(input, output)
def __init__(self): SupervisedDataSet.__init__(self, 2, 1) self.addSample([7,28],[743]) self.addSample([5,28],[701]) self.addSample([8,28],[676]) self.addSample([8,28],[641]) self.addSample([7,28],[642]) self.addSample([8,28],[671]) self.addSample([8,28],[659]) self.addSample([8,29],[629]) self.addSample([8,30],[596]) self.addSample([8,29],[550]) self.addSample([10,30],[533]) self.addSample([11,30],[499]) self.addSample([413,28],[528]) self.addSample([12,30],[567]) self.addSample([10,29],[574])
def __init__(self): SupervisedDataSet.__init__(self, 3, 1) for file_name, expected in DATA: with open(file_name, 'rb') as csvfile: csvreader = csv.reader(csvfile, delimiter=',') for row in csvreader: row_data = [] for d in row: try: row_data.append(float(d)) except ValueError: pass if row_data: self.addSample([row_data[i] for i in [0, 6, 9]], [expected]) #self.addSample(row_data, [expected]) print self
def __init__(self, inp, target=1, nb_classes=0, class_labels=None): """Initialize an empty dataset. `inp` is used to specify the dimensionality of the input. While the number of targets is given by implicitly by the training samples, it can also be set explicity by `nb_classes`. To give the classes names, supply an iterable of strings as `class_labels`.""" # FIXME: hard to keep nClasses synchronized if appendLinked() etc. is used. SupervisedDataSet.__init__(self, inp, target) self.addField('class', 1) self.nClasses = nb_classes if len(self) > 0: # calculate class histogram, if we already have data self.calculateStatistics() self.convertField('target', int) if class_labels is None: self.class_labels = list(set(self.getField('target').flatten())) else: self.class_labels = class_labels # copy classes (may be changed into other representation) self.setField('class', self.getField('target'))
def __init__(self, imgnames=None): SupervisedDataSet.__init__(self, 10*15, 1) ''' if imgnames==None: imgnames = os.listdir('./dataset') map(lambda a: './dataset/'+a, imgnames) ''' imgnames.sort() for iname in imgnames: img = Image.open(iname) w,h = img.size assert(w*h==150) pixels=[] for i in range(w): for j in range(h): p = img.getpixel((i,j)) #All the 3 fields of p are equal, always. #Therefore we need only one to represent. pixels.append(float(p[0])/255) num = iname[rfind(iname,'/')+1:rfind(iname,'.')] assert(len(pixels)==150) self.addSample(pixels, [int(num)])
def __init__(self): SupervisedDataSet.__init__(self, 2, 1) self.addSample([0,0],[0]) self.addSample([0,1],[1]) self.addSample([1,0],[1]) self.addSample([1,1],[0])
def __init__(self): SupervisedDataSet.__init__(self, 2, 1) self.addSample([0, 0], [0]) self.addSample([0, 1], [1]) self.addSample([1, 0], [1]) self.addSample([1, 1], [0])