NYC_BIKE/generate_data.py

import numpy as np
import pandas as pd
import h5py
import torch

# class StandardScaler:
#     """
#     Standard the input
#     """
#
#     def __init__(self, mean, std):
#         self.mean = mean
#         self.std = std
#
#     def transform(self, data):
#         return (data - self.mean) / self.std
#
#     def inverse_transform(self, data):
#         if type(data) == torch.Tensor and type(self.mean) == np.ndarray:
#             self.std = torch.from_numpy(self.std).to(data.device).type(data.dtype)
#             self.mean = torch.from_numpy(self.mean).to(data.device).type(data.dtype)
#         return (data * self.std) + self.mean
#
# f_bike = h5py.File('bike_data.h5','r')
# f_bike.keys()
# print([key for key in f_bike.keys()])
#
# f_graph= h5py.File('all_graph.h5','r')
# f_graph.keys()
# print([key for key in f_graph.keys()])
# bike_graph = f_graph['dis_bb'][:]
# print(f_graph['pcc_bb'][:].shape, f_graph['pcc_bb'][:])
# print(f_graph['trans_bb'][:].shape, f_graph['trans_bb'][:])
# bike_drop = np.expand_dims(f_bike['bike_drop'][:], axis=-1)
# bike_pick = np.expand_dims(f_bike['bike_pick'][:], axis=-1)
#
# print(bike_drop[bike_drop==0].shape, bike_pick[bike_pick==0].shape)
#
# bike_demand = np.concatenate([bike_drop, bike_pick], axis=-1)
# # np.savez('NYC_BIKE.npz', data=bike_demand)

datas = np.load('NYC_BIKE.npz')
print(datas.files)
print(datas['data'])
print(datas['data'].shape)

# np.savetxt("NYC_BIKE.csv", bike_graph, delimiter=",")
A = pd.read_csv("NYC_BIKE.csv", header=None).values.astype(np.float32)
print(A.shape)
print(A)


# k = datas['data']
# print(k.dtype, k[k==0].shape, k[k<0.1].shape)
# scaler_data = StandardScaler(k.mean(), k.std())
# k = torch.FloatTensor(k)
# trans1 = scaler_data.transform(k)
# trans2 = scaler_data.inverse_transform(trans1)
#
# print(trans2.dtype, trans2[trans2==0].shape)