# k-nearest neighbors for multioutput regression
from sklearn.neighbors import KNeighborsRegressor
import pickle
from tools import *

model = KNeighborsRegressor(algorithm='ball_tree', leaf_size=20, metric='euclidean', n_neighbors=6, weights='distance')

def train_model(scanmap, filename):
    # x --> vectors of RSSI values
    x = []
    # y --> coordinates of locations
    y = []
    x, y = get_model_inputs(scanmap)

    print("Training vector length: ", len(x[0]))
    model.fit(x, y)
    # save the model to disk
    pickle.dump(model, open(filename, 'wb'))
    return True


def get_trained_model():
    loaded_model = pickle.load(open(filename, 'rb'))
    return loaded_model


def get_prediction(testvector, filename):
    # load the model from disk
    loaded_model = pickle.load(open(filename, 'rb'))
    # retrieve AP list
    retrieve_aplist()
    testvector = parse_vector_string(testvector)
    print("Test vector: ", len(testvector), testvector)
    testvector = filter_aps(testvector)
    print("Test vector: ", len(testvector), testvector)
    testvector = clean_vectors([testvector])
    print("Test vector: ", len(testvector), testvector, len(testvector[0]))
    result = loaded_model.predict(testvector)
    return result

# Hyperparameter tuning
def optimize(x, y):
    model=KNeighborsRegressor()
    params={'n_neighbors': [3, 4, 5, 6],
        'weights': ['uniform', 'distance'],
        'algorithm': ['brute', 'kd_tree', 'ball_tree'],
        'metric': ['manhattan', 'euclidean', 'chebyshev', 'minkowski'],
        'leaf_size': [20, 30, 40]}

    gcv=GridSearchCV(model, param_grid=params, verbose=1)
    gcv.fit(x, y)
    # The best hyper parameters set
    print("Best Hyper Parameters:\n", gcv.best_params_)
    return gcv.best_estimator_

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def train_model(scanmap, filename):
    x = []
    y = []
    x, y = get_model_inputs(scanmap)
    print('Training vector length: ', len(x[0]))
    model.fit(x, y)
    pickle.dump(model, open(filename, 'wb'))
    return True


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def get_prediction(testvector, filename):
    loaded_model = pickle.load(open(filename, 'rb'))
    retrieve_aplist()
    testvector = parse_vector_string(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = filter_aps(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = clean_vectors([testvector])
    print('Test vector: ', len(testvector), testvector, len(testvector[0]))
    result = loaded_model.predict(testvector)
    return result


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def train_model(scanmap, filename):
    x = []
    y = []
    x, y = get_model_inputs(scanmap)
    print('Training vector length: ', len(x[0]))
    model.fit(x, y)
    pickle.dump(model, open(filename, 'wb'))
    return True


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def get_prediction(testvector, filename):
    loaded_model = pickle.load(open(filename, 'rb'))
    retrieve_aplist()
    testvector = parse_vector_string(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = filter_aps(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = clean_vectors([testvector])
    print('Test vector: ', len(testvector), testvector, len(testvector[0]))
    result = loaded_model.predict(testvector)
    return result


def optimize(x, y):
    model = KNeighborsRegressor()
    params = {'n_neighbors': [3, 4, 5, 6], 'weights': ['uniform',
        'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'],
        'metric': ['manhattan', 'euclidean', 'chebyshev', 'minkowski'],
        'leaf_size': [20, 30, 40]}
    gcv = GridSearchCV(model, param_grid=params, verbose=1)
    gcv.fit(x, y)
    print('Best Hyper Parameters:\n', gcv.best_params_)
    return gcv.best_estimator_

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def train_model(scanmap, filename):
    x = []
    y = []
    x, y = get_model_inputs(scanmap)
    print('Training vector length: ', len(x[0]))
    model.fit(x, y)
    pickle.dump(model, open(filename, 'wb'))
    return True


def get_trained_model():
    loaded_model = pickle.load(open(filename, 'rb'))
    return loaded_model


def get_prediction(testvector, filename):
    loaded_model = pickle.load(open(filename, 'rb'))
    retrieve_aplist()
    testvector = parse_vector_string(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = filter_aps(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = clean_vectors([testvector])
    print('Test vector: ', len(testvector), testvector, len(testvector[0]))
    result = loaded_model.predict(testvector)
    return result


def optimize(x, y):
    model = KNeighborsRegressor()
    params = {'n_neighbors': [3, 4, 5, 6], 'weights': ['uniform',
        'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'],
        'metric': ['manhattan', 'euclidean', 'chebyshev', 'minkowski'],
        'leaf_size': [20, 30, 40]}
    gcv = GridSearchCV(model, param_grid=params, verbose=1)
    gcv.fit(x, y)
    print('Best Hyper Parameters:\n', gcv.best_params_)
    return gcv.best_estimator_

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model = KNeighborsRegressor(algorithm='ball_tree', leaf_size=20, metric=
    'euclidean', n_neighbors=6, weights='distance')


def train_model(scanmap, filename):
    x = []
    y = []
    x, y = get_model_inputs(scanmap)
    print('Training vector length: ', len(x[0]))
    model.fit(x, y)
    pickle.dump(model, open(filename, 'wb'))
    return True


def get_trained_model():
    loaded_model = pickle.load(open(filename, 'rb'))
    return loaded_model


def get_prediction(testvector, filename):
    loaded_model = pickle.load(open(filename, 'rb'))
    retrieve_aplist()
    testvector = parse_vector_string(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = filter_aps(testvector)
    print('Test vector: ', len(testvector), testvector)
    testvector = clean_vectors([testvector])
    print('Test vector: ', len(testvector), testvector, len(testvector[0]))
    result = loaded_model.predict(testvector)
    return result


def optimize(x, y):
    model = KNeighborsRegressor()
    params = {'n_neighbors': [3, 4, 5, 6], 'weights': ['uniform',
        'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'],
        'metric': ['manhattan', 'euclidean', 'chebyshev', 'minkowski'],
        'leaf_size': [20, 30, 40]}
    gcv = GridSearchCV(model, param_grid=params, verbose=1)
    gcv.fit(x, y)
    print('Best Hyper Parameters:\n', gcv.best_params_)
    return gcv.best_estimator_

# k-nearest neighbors for multioutput regression
from sklearn.neighbors import KNeighborsRegressor
import pickle
from tools import *

model = KNeighborsRegressor(algorithm='ball_tree', leaf_size=20, metric='euclidean', n_neighbors=6, weights='distance')

def train_model(scanmap, filename):
    # x --> vectors of RSSI values
    x = []
    # y --> coordinates of locations
    y = []
    x, y = get_model_inputs(scanmap)

    print("Training vector length: ", len(x[0]))
    model.fit(x, y)
    # save the model to disk
    pickle.dump(model, open(filename, 'wb'))
    return True


def get_trained_model():
    loaded_model = pickle.load(open(filename, 'rb'))
    return loaded_model


def get_prediction(testvector, filename):
    # load the model from disk
    loaded_model = pickle.load(open(filename, 'rb'))
    # retrieve AP list
    retrieve_aplist()
    testvector = parse_vector_string(testvector)
    print("Test vector: ", len(testvector), testvector)
    testvector = filter_aps(testvector)
    print("Test vector: ", len(testvector), testvector)
    testvector = clean_vectors([testvector])
    print("Test vector: ", len(testvector), testvector, len(testvector[0]))
    result = loaded_model.predict(testvector)
    return result

# Hyperparameter tuning
def optimize(x, y):
    model=KNeighborsRegressor()
    params={'n_neighbors': [3, 4, 5, 6],
        'weights': ['uniform', 'distance'],
        'algorithm': ['brute', 'kd_tree', 'ball_tree'],
        'metric': ['manhattan', 'euclidean', 'chebyshev', 'minkowski'],
        'leaf_size': [20, 30, 40]}

    gcv=GridSearchCV(model, param_grid=params, verbose=1)
    gcv.fit(x, y)
    # The best hyper parameters set
    print("Best Hyper Parameters:\n", gcv.best_params_)
    return gcv.best_estimator_
