如何计算 PLC 和 SRC

如何解决如何计算 PLC 和 SRC

我将 Extream 学习机用于回归数据，我想计算 y_test 和预测之间的 Spearmans 等级相关性和 Pearsons 线性相关性……我需要 python 代码来计算 SPCC 和 PLCC。 请任何人可以帮助我

我的代码：

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from scipy.linalg import pinv2
 import time

#import dataset
train = pd.read_excel('INRStrai.xlsx')
test = pd.read_excel('INRStes.xlsx')

#scaler data
scaler = MinMaxScaler()
X_train = scaler.fit_transform(train.values[:,1:])
y_train = scaler.fit_transform(train.values[:,:1])
X_test = scaler.fit_transform(test.values[:,1:])
y_test = scaler.fit_transform(test.values[:,:1])

#input size
input_size = X_train.shape[1]

#Number of neurons
hidden_size = 300

#weights & biases
input_weights = np.random.normal(size=[input_size,hidden_size])
biases = np.random.normal(size=[hidden_size])

#Activation Function
def relu(x):
   return np.maximum(x,x)

#Calculations
def hidden_nodes(X):
    G = np.dot(X,input_weights)
    G = G + biases
    H = relu(G)

from datetime import timedelta
start_time = time.time()

# Perform lots of computations.
elapsed_time_secs = time.time() - start_time

msg = "Execution took: %s secs (Wall clock time)" % timedelta(seconds=round(elapsed_time_secs))
print(msg)