import numpy as np
a = np.mat('1, 2, 3;4, 5, 6')
print(a)
print(type(a))
print("=================================")
b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)
print(type(b))

np.save('a.npy', a)
np.save('b.npy', b)??????????????????

data1 = np.load('a.npy')
data2 = np.load('b.npy')

print(data1)
print(type(data1))
print("=================================")
print(data2)
print(type(data2))

import numpy as np
from scipy import io
a = np.mat('1, 2, 3;4, 5, 6')
print(a)
print(type(a))
print("=================================")
b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)
print(type(b))

io.savemat('a.mat', {'matrix': a})
io.savemat('b.mat', {'array': b})

data1 = io.loadmat('a.mat')
print(data1)
print(type(data1))
print("=================================")
data2 = io.loadmat('b.mat')
print(data2)
print(type(data2))

print(data1['matrix'])
print(type(data1['matrix']))
print("=================================")
print(data2['array'])
print(type(data2['array']))

import numpy as np
from scipy import io

mat = io.loadmat('yourfile.mat')
# 如果報錯:Please use HDF reader for matlab v7.3 files
# 改為下一種方式讀取
import h5py
mat = h5py.File('yourfile.mat')

# mat文件里可能有多個cell，各對應(yīng)著一個dataset

# 可以用keys方法查看cell的名字, 現(xiàn)在要用list(mat.keys()),
# 另外，讀取要用data = mat.get('名字'), 然后可以再用Numpy轉(zhuǎn)為array
print(mat.keys())
# 可以用values方法查看各個cell的信息
print(mat.values())

# 可以用shape查看維度信息
print(mat['your_dataset_name'].shape)
# 注意，這里看到的shape信息與你在matlab打開的不同
# 這里的矩陣是matlab打開時矩陣的轉(zhuǎn)置
# 所以，我們需要將它轉(zhuǎn)置回來
mat_t = np.transpose(mat['your_dataset_name'])
# mat_t 是numpy.ndarray格式

# 再將其存為npy格式文件
np.save('yourfile.npy', mat_t)

import numpy as np
matrix = np.load('yourfile.npy')

import numpy as np

matrix = np.load('yourfile.npy')
f = h5py.File('yourfile.mat', 'w')
f.create_dataset('dataname', data=matrix)
# 這里不會將數(shù)據(jù)轉(zhuǎn)置

from scipy import io

mat = np.load('rlt_gene_features.npy-layer-3-train.npy')
io.savemat('gene_features.mat', {'gene_features': mat})