我想将用Python编写的代码转换为Matlab代码。我可以知道是否有可能这样做。我很想知道，我们如何在 Matlab 中使用 python 库。共享进行转换的过程
这是我使用的数据：
https://drive.google.com/open?id=1GLm87-5E_6YhUIPZ_CtQLV9F9wcGaTj2
这是我在Python中的代码：
# imports libraries
import numpy as np
import pandas as pd
import os
import tensorflow as tf
import matplotlib.pyplot as plt
import random
from scipy import signal
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.models import Sequential
from tensorflow import set_random_seed
from tensorflow.keras.initializers import glorot_uniform
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import accuracy_score
from importlib import reload
# useful pandas display settings
pd.options.display.float_format = '{:.3f}'.format
# useful functions
def plot_history(history, metrics_to_plot):
"""
Function plots history of selected metrics for fitted neural net.
"""
# plot
for metric in metrics_to_plot:
plt.plot(history.history[metric])
# name X axis informatively
plt.xlabel('epoch')
# name Y axis informatively
plt.ylabel('metric')
# add informative legend
plt.legend(metrics_to_plot)
# plot
plt.show()
def plot_fit(y_true, y_pred, title='title'):
"""
Function plots true values and predicted values, sorted in increase order by true values.
"""
# create one dataframe with true values and predicted values
results = y_true.reset_index(drop=True).merge(pd.DataFrame(y_pred), left_index=True, right_index=True)
# rename columns informartively
results.columns = ['true', 'prediction']
# sort for clarity of visualization
results = results.sort_values(by=['true']).reset_index(drop=True)
# plot true values vs predicted values
results.plot()
# adding scatter on line plots
plt.scatter(results.index, results.true, s=5)
plt.scatter(results.index, results.prediction, s=5)
# name X axis informatively
plt.xlabel('obs sorted in ascending order with respect to true values')
# add customizable title
plt.title(title)
# plot
plt.show();
def reset_all_randomness():
"""
Function assures reproducibility of NN estimation results.
"""
# reloads
reload(tf)
reload(np)
reload(random)
# seeds - for reproducibility
os.environ['PYTHONHASHSEED']=str(984797)
random.seed(984797)
set_random_seed(984797)
np.random.seed(984797)
my_init = glorot_uniform(seed=984797)
return my_init
def give_me_mse(true, prediction):
"""
This function returns mse for 2 vectors: true and predicted values.
"""
return np.mean((true-prediction)**2)
# Importing the dataset
X = pd.read_excel(r"C:filelocationData.xlsx","Sheet1").values
y = pd.read_excel(r"C:filelocationData.xlsx","Sheet2").values
# Importing the experiment data
Data = pd.read_excel(r"C:filelocationData.xlsx","Sheet1")
v = pd.DataFrame(Data, columns= ['v']).values
c = pd.DataFrame(Data, columns= ['c']).values
ird = pd.DataFrame(Data, columns= ['ird']).values
tmp = pd.DataFrame(Data, columns= ['tmp']).values
#Data Prepration
ird = ird.ravel()
tmp = tmp.ravel()
ir = np.nanmax(ird)
tp = np.nanmax(tmp)
p = v*c
p = p.ravel()
peaks, _ = signal.find_peaks(p)
nop = len(peaks)
pv = p.max()
#Experimental Data for testing
E_data = np.array([[ir,tp,pv,nop]])
#importing some more libraries
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(np.ravel(y))
y_encoded = encoder.transform(np.ravel(y))
# convert integers to dummy variables (i.e. one hot encoded)
y_dummy = np_utils.to_categorical(y_encoded)
# reset_all_randomness - for reproducibility
my_init = reset_all_randomness()
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test, y_train_dummy, y_test_dummy = train_test_split(X, y, y_dummy, test_size = 0.3, random_state = 20)
# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
E_data = sc.transform(E_data)
# Initialising the ANN
model0 = Sequential()
# Adding 1 hidden layer: the input layer and the first hidden layer
model0.add(Dense(units = 160, activation = 'tanh', input_dim = 4, kernel_initializer=my_init))
# Adding 2 hidden layer
model0.add(Dense(units = 49, activation = 'tanh', kernel_initializer=my_init))
# Adding 3 hidden layer
model0.add(Dense(units = 24, activation = 'tanh', kernel_initializer=my_init))
# Adding 4 hidden layer
model0.add(Dense(units = 15, activation = 'tanh', kernel_initializer=my_init))
# Adding output layer
model0.add(Dense(units = 6, activation = 'softmax', kernel_initializer=my_init))
# Set up Optimizer
Optimizer = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.99)
# Compiling the ANN
model0.compile(optimizer = Optimizer, loss = 'categorical_crossentropy', metrics=['accuracy','categorical_crossentropy','mse'])
# Fitting the ANN to the Train set, at the same time observing quality on Valid set
history = model0.fit(X_train, y_train_dummy, validation_data=(X_test, y_test_dummy), batch_size = 100, epochs = 1500)
# Generate prediction for all Train, Valid set and Experimental set
y_train_pred_model0 = model0.predict(X_train)
y_test_pred_model0 = model0.predict(X_test)
y_exp_pred_model0 = model0.predict(E_data)
# find final prediction by taking class with highest probability
y_train_pred_model0 = np.array([[list(x).index(max(list(x))) + 1] for x in y_train_pred_model0])
y_test_pred_model0 = np.array([[list(x).index(max(list(x))) + 1] for x in y_test_pred_model0])
y_exp_pred_model0 = np.array([[list(x).index(max(list(x))) + 1] for x in y_exp_pred_model0])
# check what metrics are in fact available in history
history.history.keys()
# Inverse scaling
X_train_inverse = sc.inverse_transform(X_train)
X_test_inverse = sc.inverse_transform(X_test)
E_data_inverse = sc.inverse_transform(E_data)
#Plots
print('#######################################################################')
# look at model fitting history
plot_history(history, ['mean_squared_error', 'val_mean_squared_error'])
plot_history(history, ['categorical_crossentropy', 'val_categorical_crossentropy'])
plot_history(history, ['acc', 'val_acc'])
# look at model fit quality
plot_fit(pd.DataFrame(y_train), y_train_pred_model0, 'Fit on train data')
plot_fit(pd.DataFrame(y_test), y_test_pred_model0, 'Fit on test data')
#Results
print('#######################################################################')
print('=============Mean Squared Error============')
print('MSE on train data is: {}'.format(give_me_mse(y_train, y_train_pred_model0)))
print('MSE on test data is: {}'.format(give_me_mse(y_test, y_test_pred_model0)))
print('#######################################################################')
print('================Accuracy===================')
print('Accuracy of ANN is: {} Percentage'.format((accuracy_score(y_test, y_test_pred_model0))*100))
print('#######################################################################')
print('========Result of Test Data set is=========')
for i in range(len(y_test)):
print('%s => %d (expected %s)' % (X_test_inverse[i].tolist(), y_test_pred_model0[i], y_test[i].tolist()))
print('#######################################################################')
print('====Result of Experimental Data set is=====')
print('%s => %d' % (E_data_inverse, y_exp_pred_model0))
没有"直接"的方法可以将Python代码转换为MATLAB代码。
您可以做的是直接转换方法(算法(并从头开始编写代码。
或者我认为对你来说更可取的是使用他们的 API 直接在 MATLAB 中调用 python 脚本
以下是进一步阅读的链接：https://in.mathworks.com/help/matlab/call-python-libraries.html
例如：
>> py.math.sqrt(4)
ans =
1
要运行自己的函数，您可以在当前的 MATLAB 工作目录中创建一个文件。 以下是包含这两行的文件"hello.py"：
def world():
return 'hello world'
然后在 MATLAB 中：
>> py.hello.world();
Hello world!
如果遇到错误，请确保使用的是受支持的 Python 版本并添加
pyversion <path_to_executable>
到 MATLAB 文件的开头。
虽然我不确定考虑到您正在导入的所有 Python 库(Scipy、Tensorflow 等(它的效果如何