def __init__(self):
self.seed = 12345
# TODO: Where should this row generating function be?
# Here, in the LazyTable or in the LazyRowInstance
self.class_vars = OrderedDict()
self.class_vars['class'] = lambda row_index: numpy.random.randint(2)
self.attributes_continuous = OrderedDict()
# TODO HACK for incremental learner.
if False:
self.attributes_continuous['X'] = lambda row_index: numpy.random.random() * 4.0 + 2.0
self.attributes_continuous['Y'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['k'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['l'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['m'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['n'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['o'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['p'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['q'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['r'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['s'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['t'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['u'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
#self.attributes_continuous['X'] = lambda row_index: numpy.random.random() * 4.0 + 2.0
#self.attributes_continuous['Y'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
#self.attributes_continuous['k'] = lambda row_index: numpy.random.random() * 1e-8 - 1e-9
#self.attributes_continuous['l'] = lambda row_index: numpy.random.random() * 1e-10 - 2e-11
#self.attributes_continuous['m'] = lambda row_index: numpy.random.random() * 1e-12 + 3e-13
#self.attributes_continuous['n'] = lambda row_index: numpy.random.random() * 1e22
#self.attributes_continuous['o'] = lambda row_index: numpy.random.random() * 1e20
#self.attributes_continuous['p'] = lambda row_index: numpy.random.random() * 1e18
#self.attributes_continuous['q'] = lambda row_index: numpy.random.random() * 1e16
#self.attributes_continuous['r'] = lambda row_index: numpy.random.random() * 1e14
#self.attributes_continuous['s'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
#self.attributes_continuous['t'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
#self.attributes_continuous['u'] = lambda row_index: numpy.random.random() * 8.0 + 10.0
self.attributes_continuous['a'] = lambda row_index: \
numpy.random.normal(loc=1.0, scale=2.0) \
if self.pull_cell(row_index, 'class') == 0 else \
numpy.random.normal(loc=5.0, scale=2.0)
self.attributes_continuous['b'] = lambda row_index: \
numpy.random.normal(loc=-1.0, scale=1.0) \
if self.pull_cell(row_index, 'class') == 0 else \
numpy.random.normal(loc=1.0, scale=0.5)
self.data = LazyTable.from_domain(domain = self.pull_domain())
self.data.widget_origin = self
self.data.name = "GeneratedTest1"
# Pull some data so non-lazy aware widgets are not confused.
self.data.pull_region_of_interest()
self.send("Data", self.data)
def we_have_a_new_table(self):
# TODO: think of better name for this function.
domain = self.pull_domain()
self.old_region_of_interest = None
#self.region_of_interest = None
data = LazyTable.from_domain(domain=domain)
data.widget_origin = self
# Stop the pulling loop in our current data, if any.
if isinstance(self.data, LazyTable):
self.data.stop_pulling = True
self.data = data
self.send("Data", self.data)
print("Orange Table send B")
def open_file(self, fn, preload_data=True):
#def open_file(self, fn, preload_rows=False):
self.error()
self.warning()
self.information()
if not os.path.exists(fn):
dirname, basename = os.path.split(fn)
if os.path.exists(os.path.join(".", basename)):
fn = os.path.join(".", basename)
self.information("Loading '{}' from the current directory."
.format(basename))
if fn == "(none)" or not 'fixed' in fn:
self.send("Data", None)
self.infoa.setText("No data loaded")
self.infob.setText("")
self.warnings.setText("")
return
self.loaded_file = fn
domain = self.pull_header()
self.infoa.setText(
'{} instance(s), {} feature(s), {} meta attributes'
.format(self.pull_length(), len(domain.attributes), len(domain.metas)))
if isinstance(domain.class_var, ContinuousVariable):
self.infob.setText('Regression; Numerical class.')
elif isinstance(domain.class_var, DiscreteVariable):
self.infob.setText('Classification; Discrete class with {} values.'
.format(len(domain.class_var.values)))
# TODO: better class_vars support ('data' is unknown at this stage).
#elif data.domain.class_vars:
# self.infob.setText('Multi-target; {} target variables.'
# .format(len(data.domain.class_vars)))
else:
self.infob.setText("Data has no target variable.")
# What does this do?
#addOrigin(data, fn)
# make new data and send it
#data = LazyTable()
#data.domain = domain
# Creating the LazyTable from the domain will ensure that
# X, Y and metas are set as well, to empty numpy arrays.
data = LazyTable.from_domain(domain)
data.widget_origin = self
# The name is necessary for the scatterplot
fName = os.path.split(fn)[1]
if "." in fName:
data.name = fName[:fName.rfind('.')]
else:
data.name = fName
# What does this do?
#self.dataReport = self.prepareDataReport(data)
self.data = data
# Ensure that some data is always available.
if preload_data:
self.pull_region_of_interest()
else:
self.send("Data", self.data)
