alexmr09
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
import init_utils
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import common
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# Initialize the environment and get the name
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name = init_utils.initialize_environment(__file__)
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args = init_utils.get_args()
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# Set arguments from command line
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max_acc_drop = args.max_acc_drop
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device = args.device
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import pandas as pd
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import numpy as np
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import torch.nn as nn
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import torch.nn.functional as F
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from sklearn.model_selection import train_test_split
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# Load our Dataset
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pelvis_acc = pd.read_excel('elderly_fall/Posterior Pelvis Accelerometer Measures.xlsx',
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sheet_name = 'ST')
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y_data = pelvis_acc['Faller (1), Non-Faller (0)'].to_numpy()
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pelvis_acc = pelvis_acc.drop(columns = [pelvis_acc.columns[0], 'Faller (1), Non-Faller (0)'])
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head_acc = pd.read_excel('elderly_fall/Head Accelerometer Measures.xlsx',
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sheet_name = 'ST')
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head_acc = head_acc.drop(columns = [head_acc.columns[0], 'Faller (1), Non-Faller (0)'])
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pressure_sen = pd.read_excel('elderly_fall/Pressure-Sensing Insole Measures.xlsx',
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sheet_name = 'ST')
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pressure_sen = pressure_sen.drop(columns = [pressure_sen.columns[0], 'Faller (1), Non-Faller (0)'])
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left_shank = pd.read_excel('elderly_fall/Left Shank Accelerometer Measures.xlsx',
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sheet_name = 'ST')
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left_shank = left_shank.drop(columns = [left_shank.columns[0], 'Faller (1), Non-Faller (0)'])
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X_data = pd.concat([head_acc, pressure_sen, pelvis_acc, left_shank], axis = 1)
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# Preprocess the Data
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def normalize_column(column):
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old_min = column.min()
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old_max = column.max()
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new_min = 0
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new_max = 255
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normalized_column = ((column - old_min) / (old_max - old_min)) * (new_max - new_min) + new_min
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return normalized_column.astype(int)
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X_data = X_data.apply(normalize_column)
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X_d = X_data/255.0
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X_train, X_test, y_train, y_test = train_test_split(X_d, y_data,
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test_size = 0.15, random_state = 42)
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X_train = X_train.to_numpy()
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X_test = X_test.to_numpy()
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BATCH_SIZE = 1
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epochs = 5
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lr = 0.0001
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class EF_MLP(nn.Module):
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def __init__(self):
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super(EF_MLP, self).__init__()
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self.linear1 = nn.Linear(117, 20)
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self.relu1 = nn.ReLU()
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self.linear2 = nn.Linear(20, 2)
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def forward(self,X):
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X = self.relu1(self.linear1(X))
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X = self.linear2(X)
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return F.log_softmax(X, dim = 1)
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net = EF_MLP()
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common.create_ibex_qnn(net, name, device, X_train, y_train, X_test, y_test, BATCH_SIZE = BATCH_SIZE,
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epochs = epochs, lr = lr, max_acc_drop = max_acc_drop)
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