def test_elmr_boston():
# load dataset
data = elm.read("tests/data/boston.data")
# create a regressor
elmr = elm.ELMRandom()
try:
# search for best parameter for this dataset
# elmr.search_param(data, cv="kfold", of="rmse")
# split data in training and testing sets
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
#train and test
tr_result = elmr.train(tr_set)
te_result = elmr.test(te_set)
except:
ERROR = 1
else:
ERROR = 0
assert (ERROR == 0)
def test_elmr_boston():
# load dataset
data = elm.read("elmTestData/boston.data")
# create a regressor
elmr = elm.ELMRandom()
try:
# search for best parameter for this dataset
# elmr.search_param(data, cv="kfold", of="rmse")
# split data in training and testing sets
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
#train and test
tr_result = elmr.train(tr_set)
te_result = elmr.test(te_set)
except:
ERROR = 1
else:
ERROR = 0
assert (ERROR == 0)
def main():
# Load in 2D velocity data
velocity = data.load_data()
# data.example_of_data(velocity)
# form testing and training sets for velocity data
X_train, y_train, X_test, y_test = data.form_train_test_sets(velocity)
# Data transformation
#print(X_test[0]['u'].shape)
print("len of y", len(y_test))
# print("shape of y", y_test.shape)
#print(y_train)
#print(X_train['u'].shape)
import elm as standard_elm
# create a classifier
elmk = standard_elm.ELMKernel()
nn_structure = [9, 100, 1]
x, y = utils.transform_dict_for_nn(X_train, y_train, nn_structure[0])
x = np.transpose(x)
y = np.transpose([y])
tr_set = np.concatenate(
(y, x), 1) #standard format for elm function - y_train + x_train
x_test, y_test = utils.transform_dict_for_nn(X_test[0], y_test[0],
nn_structure[0])
#x_test = np.transpose(x_test)
#y_test = np.transpose([y_test])
#te_set = np.concatenate((y_test, x_test), 1)
# load dataset
dataa = standard_elm.read("boston.data")
# create a classifier
elmk = standard_elm.elmk.ELMKernel()
# split data in training and testing sets
# use 80% of dataset to training and shuffle data before splitting
tr_set, te_set = standard_elm.split_sets(dataa,
training_percent=.8,
perm=True)
#train and test
# results are Error objects
tr_result = elmk.train(tr_set)
te_result = elmk.test(te_set)
print(te_result.get_accuracy())
te_result.predicted_targets
def test_elmk_iris():
# load dataset
data = elm.read("tests/data/iris.data")
# create a regressor
elmk = elm.ELMKernel()
try:
# search for best parameter for this dataset
elmk.search_param(data, cv="kfold", of="accuracy", eval=10)
# split data in training and testing sets
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
#train and test
tr_result = elmk.train(tr_set)
te_result = elmk.test(te_set)
except:
ERROR = 1
else:
ERROR = 0
assert (ERROR == 0)
def test_elmr_iris():
# load dataset
data = elm.read("tests/data/iris.data")
# create a regressor
elmr = elm.ELMRandom()
try:
# search for best parameter for this dataset
elmr.search_param(data, cv="kfold", of="accuracy", eval=10)
# split data in training and testing sets
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
#train and test
tr_result = elmr.train(tr_set)
te_result = elmr.test(te_set)
except:
ERROR = 1
else:
ERROR = 0
assert (ERROR == 0)
def load(self):
data = elm.read(self.__full_path)
x = data[:, 1:]
y = data[:, 0]
return x, y
# split data in training and testing sets
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
#train and test
tr_result = elmr.train(tr_set)
te_result = elmr.test(te_set)
print(tr_result.get_accuracy)
print(te_result.get_accuracy)
except:
ERROR = 1
else:
ERROR = 0
assert (ERROR == 0)
# assert (te_result.get_rmse() <= 70)
if __name__ == '__main__':
# load dataset
data = elm.read("elmTestData/diabetes.data")
# create a regressor
elmr = elm.ELMRandom()
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
# train and test
tr_result = elmr.train(tr_set)
te_result = elmr.test(te_set)
print(tr_result.get_rmse())
print(te_result.get_rmse())