def linsvm_fit(input_dict):
from discomll.classification import linear_svm
fitmodel_url = linear_svm.fit(input_dict["dataset"],
nu=input_dict["nu"],
save_results=True)
return {"fitmodel_url": fitmodel_url}
train = dataset.Data(data_tag=[
"http://ropot.ijs.si/data/sonar/train/xaaaaa.gz",
"http://ropot.ijs.si/data/sonar/train/xaaabj.gz"
],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 61),
id_index=0,
y_index=61,
X_meta=["c" for i in range(1, 61)],
y_map=["R", "M"],
delimiter=",")
test = dataset.Data(data_tag=[
"http://ropot.ijs.si/data/sonar/test/xaaaaa.gz",
"http://ropot.ijs.si/data/sonar/test/xaaabj.gz"
],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 61),
id_index=0,
y_index=61,
X_meta=["c" for i in range(1, 61)],
y_map=["R", "M"],
delimiter=",")
fit_model = linear_svm.fit(train)
predictions = linear_svm.predict(test, fit_model)
print predictions
def linsvm_fit(input_dict):
from discomll.classification import linear_svm
fitmodel_url = linear_svm.fit(input_dict["dataset"],
nu=input_dict["nu"],
save_results=True)
return {"fitmodel_url": fitmodel_url}
from discomll import dataset
from discomll.classification import linear_svm
train = dataset.Data(
data_tag=["http://ropot.ijs.si/data/sonar/train/xaaaaa.gz", "http://ropot.ijs.si/data/sonar/train/xaaabj.gz"],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 61),
id_index=0,
y_index=61,
X_meta=["c" for i in range(1, 61)],
y_map=["R", "M"],
delimiter=",")
test = dataset.Data(
data_tag=["http://ropot.ijs.si/data/sonar/test/xaaaaa.gz", "http://ropot.ijs.si/data/sonar/test/xaaabj.gz"],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 61),
id_index=0,
y_index=61,
X_meta=["c" for i in range(1, 61)],
y_map=["R", "M"],
delimiter=",")
fit_model = linear_svm.fit(train)
predictions = linear_svm.predict(test, fit_model)
print predictions
