qq_39023633的博客

import numpy as npimport matplotlib.pyplot as pltfrom scipy.special import expit as f_actdef init_net(): input_nodes = 784 print('Input the number of hidden neurons:') hidden_nodes = 500#int(input()) out_nodes = 10 print('Input the.

import numpy as npfrom sklearn.decomposition import PCAfrom sklearn import datasetsimport matplotlibimport matplotlib.pyplot as plt#加载数据data = np.loadtxt(open("./data/task1.csv","r"),delimiter=",",skiprows=0)#设置主成分参数：pc个数，数值求解器的类型pca = PCA(n_c.

from sklearn.preprocessing import LabelEncoderfrom sklearn.svm import LinearSVCfrom sklearn.metrics import classification_report, confusion_matrixfrom sklearn.model_selection import train_test_splitfrom IPython.display import Imagefrom imutils import.

#%%import pandas as pdimport matplotlibimport numpy as npimport matplotlib.pyplot as pltdf = pd.read_csv('./data/d.csv')task_data = df.head(640)#1print(len(task_data[task_data['Outcome'] == 0]))train = task_data.head(int(len(task_data)*0.8))te.

1:Let 𝑆 be a set of states, and 𝐴(𝑠), 𝑠 ∈ 𝑆, be a set of actions available in the state 𝑠.2:Initialize𝑞(𝑠,𝑎),𝑠∈𝑆,𝑠isnotterminal,𝑎∈𝐴(𝑠)arbitrarily3:Initialize 𝛼 and 𝛾4:for each game do5:Initialize a nonterminal state 𝑠0 at

from imutils import pathsimport numpy as npimport cv2import osdef extract_histogram(image, bins=(8, 8, 8)): hist = cv2.calcHist([image], [0, 1, 2], None, bins, [0, 256, 0, 256, 0, 256]) cv2.normalize(hist, hist) return hist.flatten()#加载.