def iris():
X, y = load_iris(return_X_y=True)
# clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')
clf = load_model() # make sure to pre-train the model first
clf.fit(X, y)
result = str(clf.predict(X[:2, :]))
print("PREDICTION", result)
return result # todo resturn as JSON
def iris():
X, y = load_iris(return_X_y=True)
clf = load_model() # make sure to pre-train the model first
print('classifier:', clf)
inputs = X[:2, :]
print('Inputs:', inputs)
clf.fit(X, y)
result = clf.predict(inputs)
print('PREDICTION:', result)
return 'PREDICTION:' + str(result) # todo return as Json
def iris():
X, y = load_iris(return_X_y=True)
clf = load_model() # make sure to pre-train the model first!
result = str(clf.predict(X[:2, :]))
print("PREDICTION", result)
return result # todo: return as JSON
def iris():
X, y = load_iris(return_X_y=True)
clf = load_model()
result = str(clf.predict(X[:2, :]))
print("PREDICTION", result)
return result
def iris():
model = load_model()
X, y = load_iris(return_X_y=True)
return str(model.predict(X[:2, :]))