def get_lin_reg(x, y):
data = Orange.data.Table("newdoc.csv")
earth_predictor = earth.EarthLearner(data)
tx1, ty1 = data.to_numpy("A/C")
# pl.plot(x,y,".r")
li = numpy.linspace(min(x), max(x), 20)
# predictions = [earth_predictor([s, "?"]) for s in li]
# pl.plot(linspace, predictions, "-b")
# pl.show()
data = Orange.data.Table("dat.tab")
earth_predictor = earth.EarthLearner(data)
X, Y = data.to_numpy("A/C")
# pl.plot(X, Y, ".r")
linspace = numpy.linspace(min(X), max(X), 20)
predictions = [earth_predictor([s, "?"]) for s in linspace]
# pl.plot(linspace, predictions, "-b")
# pl.show()
f = open("orangetrend.txt", "w+")
for i in range(len(linspace)):
# print linspace
# exit(0)
f.write(str(linspace[i]) + "," + str(predictions[i]) + "\n")
# f.write(str(predictions[i])+"\n")
f.close()
def test_bagged_evimp(self, dataset):
from Orange.ensemble.bagging import BaggedLearner
bagged_learner = BaggedLearner(earth.EarthLearner(terms=10, degree=2),
t=5)
bagged_classifier = bagged_learner(dataset)
evimp = earth.bagged_evimp(bagged_classifier, used_only=False)
def apply(self):
learner = earth.EarthLearner(
degree=self.degree,
terms=self.terms if self.terms >= 2 else None,
penalty=self.penalty,
name=self.name)
predictor = None
basis_matrix = None
if self.preprocessor:
learner = self.preprocessor.wrapLearner(learner)
self.error(0)
if self.data is not None:
try:
predictor = learner(self.data)
predictor.name = self.name
except Exception, ex:
self.error(0, "An error during learning: %r" % ex)
if predictor is not None:
base_features = predictor.base_features()
basis_domain = Orange.data.Domain(base_features,
self.data.domain.class_var,
self.data.domain.class_vars)
basis_domain.add_metas(self.data.domain.get_metas())
basis_matrix = Orange.data.Table(basis_domain, self.data)
def test_multi_target_on_data(self, dataset):
self.learner = earth.EarthLearner(degree=2, terms=10)
self.predictor = self.multi_target_test(self.learner, dataset)
self.assertTrue(bool(self.predictor.multitarget))
s = str(self.predictor)
self.assertEqual(s, self.predictor.to_string())
self.assertNotEqual(s, self.predictor.to_string(3, 6))
def apply(self):
learner = earth.EarthLearner(
degree=self.degree,
terms=self.terms if self.terms >= 2 else None,
penalty=self.penalty,
name=self.name)
predictor = None
if self.preprocessor:
learner = self.preprocessor.wrapLearner(learner)
self.error(0)
if self.data is not None:
try:
predictor = learner(self.data)
predictor.name = self.name
except Exception, ex:
self.error(0, "An error during learning: %r" % ex)
os.chdir("C:/Documents and Settings/amcelhinney/My Documents/GitHub/MCS507ProjectTwo/data/")
# Remember to add the "class" keyword to the third line, under the target variable. See here:
# http://orange.biolab.si/doc/tutorial/load-data/
data = orange.ExampleTable("painted_data_wo_outlier")
print data.domain.attributes
print data[:4]
X, Y = data.to_numpy("A/C")
pl.plot(X, Y, ".r")
pl.title('Example Data Set')
pl.show()
earth_predictor = earth.EarthLearner(data)
print earth_predictor
linspace = numpy.linspace(min(X), max(X), 20)
predictions = [earth_predictor([s, "?"]) for s in linspace]
pl.plot(X, Y, ".r")
pl.plot(linspace, predictions, "-b")
pl.title('Example Data Set with Line Fit by MARS')
pl.show()
x=[]; y=[]
for i in range(len(X)):
#print i
x.append(float(X[i]))
y.append(float(Y[i]))
def setUp(self):
self.learner = earth.EarthLearner(degree=2, terms=10)
os.chdir(
"C:/Documents and Settings/amcelhinney/My Documents/GitHub/MCS507ProjectTwo/data/"
)
os.getcwd()
# Bring in the data using the C4.5 format, which brings in the .data file and then the .names file
data = orange.ExampleTable("auto-mpg")
print data.domain.attributes
# Divide data into training and validation data sets
index = Orange.data.sample.SubsetIndices2(p0=0.70)
ind = index(data)
data_training = data.select(ind, 0)
data_validation = data.select(ind, 1)
earth_predictor = earth.EarthLearner(data_training)
print earth_predictor
estimated = earth_predictor.predict(list(data_validation))
dl = list(data_validation)
# Predict all the data
estimated = []
for i in range(0, len(dl)):
t = earth_predictor.predict(dl[i])
estimated.append(t[0])
X, Y = data.to_numpy("A/C")
for i in range(0, len(names)):
