def hello_world():
# load the iris datasets
dataset = datasets.load_test_case()
X = dataset.data
y = dataset.target
# fit a logistic regression model to the data
response = []
coordinates = []
model = LogisticRegression()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
predicted = model.fit(X_train, y_train).predict(X_test)
i = 1
print X_test
for x in X_test:
print i
for y in x:
print y
response.append({"id": int(y)})
break
print json.dumps(response)
resp = Response(json.dumps(response))
resp.headers['Access-Control-Allow-Origin'] = '*'
# make predictions
return resp
# Logistic Regression from sklearn import datasets from sklearn import metrics from sklearn.naive_bayes import GaussianNB from sklearn.cross_validation import train_test_split # load the iris datasets dataset = datasets.load_test_case() X = dataset.data y = dataset.target # fit a logistic regression model to the data model = GaussianNB() X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) predicted = model.fit(X_train, y_train).predict(X_test) print(predicted) # make predictions print(metrics.classification_report(y_test, predicted)) print(metrics.confusion_matrix(y_test, predicted))