ANN

import pandas as pd 

from sklearn import datasets

from sklearn.model_selection import train_test_split 

from sklearn.linear_model import LinearRegression  

import numpy as np  

import matplotlib.pyplot as plt

from sklearn.metrics import mean_squared_error

from keras.models import Sequential

from keras.layers import Dense

from keras.wrappers.scikit_learn import KerasRegressor

def clean_dataset(df):

    assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"

    df.dropna(inplace=True)

    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)

    return df[indices_to_keep].astype(np.float64)

def baseline_model():

  # create model

 model = Sequential()

 model.add(Dense(20, input_dim=7, kernel_initializer='normal', activation='relu'))

 model.add(Dense(40, kernel_initializer='normal', activation='relu'))

 model.add(Dense(2, kernel_initializer='normal'))

 # Compile model

 model.compile(loss='mean_squared_error', optimizer='adam')

 return model

dataset = pd.read_csv('Total.csv',names = ['alpha','PIR', 'ptt', 'bpmax' ,'bpmin', 'hrfinal', 'ih', 'il', 'meu'])

X = dataset[['alpha','PIR', 'ptt', 'hrfinal', 'ih', 'il', 'meu']]

y = dataset[['bpmax','bpmin']]

#X2 = dataset2[['Ptt','ih','il','hr','v','w']]

#test = datset2[['firstaxis_1','firstaxis_2','firstaxis_3','secondaxis_1','secondaxis_2','secondaxis_3','thirdaxis_1','thirdaxis_2','thirdaxis_3','eigen1','eigen2','eigen3','firstaxis_len','secondaxis_len','thirdaxis_len','c1','c2','c3','mer_ecc','eq_ecc','age']]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) 

#print X_test

from sklearn.preprocessing import StandardScaler

sc_X=StandardScaler()

x_train=sc_X.fit_transform(X_train)

x_test=sc_X.transform(X_test)

estimator = KerasRegressor(build_fn=baseline_model, epochs=1000, batch_size=10, verbose=2)

estimator.fit(x_train, y_train)

y_pred = estimator.predict(x_test)

#print y_test

#print regressor.coef_

# print('Coefficients: \n', regressor.coef_)

# # The mean squared error

from sklearn import metrics  

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  

print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  

print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))