df['V2'] = 1
df['V3'] = df[which_conc_feature]**2
df['V4'] = df[which_conc_feature]**3
df['V5'] = df[which_conc_feature]**4
else:
print("invalid flann. check spelling")
return str_path, df
str_path, df_normed_expanded = which_flann(df_normed, 'power')
#e.g. power/dataout/JV1_1
os.makedirs(str_path + "/" + exp_str + "/figures", exist_ok=True)
feature_list = [which_conc_feature, 'V2', 'V3', 'V4', 'V5']
xdftrain, ydftrain, xdftest, ydftest, xtrain, ytrain, xtest, ytest = split_tt(
df_normed_expanded, 70, 102, *feature_list)
print("Shape of Training feature matrix as array: ", np.shape(xtrain))
print("Shape of Target array: ", np.shape(ytrain))
print("Dimension of training data array: ", np.ndim(xtrain))
print("Dimension of target array: ", np.ndim(ytrain))
#import multi-layer perceptron regressor from sklearn
from keras.models import Sequential
from keras.layers import Dense, Activation
import sklearn.metrics as skm
dim_output = 1
dim_input = 5
len_train_input = ytrain.shape[0]
#import multi-layer perceptron regressor from sklearn
df['wavelength3']=df[which_conc_feature]**2
df['wavelength4']=df[which_conc_feature]**3
df['wavelength5']=df[which_conc_feature]**4
else:
print("invalid flann. check spelling")
return str_path, df
str_path, df_normed_expanded = which_flann(df_normed, 'chebyshev')
1
os.makedirs(str_path + "/"+ exp_str +"/figures", exist_ok=True)
feature_list=[which_conc_feature ,'wavelength2','wavelength3','wavelength4','wavelength5']
xdftrain, ydftrain, xdftest, ydftest, xtrain, ytrain, xtest, ytest =
split_tt(df_normed_expanded,70,102, *feature_list)
print("Shape of Training feature matrix as array: ",np.shape(xtrain))
print("Shape of Target array: ",np.shape(ytrain))
print("Dimension of training data array: ",np.ndim(xtrain))
print("Dimension of target array: ",np.ndim(ytrain))
from keras.models import Sequential
from keras.layers import Dense, Activation
import sklearn.metrics as skm
dim_output = 1
dim_input = 5
len_train_input = ytrain.shape[0]