x = np.array([123,172,76,161,314,77,226,330,202,321,260,155,120,163,221,400,45,106,296,125,210,66,131,242,372]) x.sort() w = int(input("请输入等宽分箱的宽度：")) max = x.max() min = x.min() m = int(math.ceil((max - min)/w)) bins = [min + k*w for k in range(0,m + 1)] x_cuts = pd.cut(x, bins, right=False) number = pd.value_counts(x_cuts) rows = number.max() width = np.full([m, rows], 0) size = x.size a = 0 for j in range(0, m-1): for bin, count in number.items(): for i in range(count): if a < size: width[j][i] = x[a] a += 1 else: break # exit loop if no more values in x print(width) The answer is incorrect. I want to konw which step is wrong?

拉格朗日插值法python运用拉格朗日插值法给空缺数据进行插值，通过调用scipy中的lagrange实现(1).zip

x = np.array([x0, x1, ..., xn]) # 数据点的横坐标 y = np.array([y0, y1, ..., yn]) # 数据点的纵坐标 # 创建拉格朗日插值多项式 lag_poly = lagrange(x, y) # 对任意插值点x_val求纵坐标 y_val = lag_poly(x_...

Python numpy线性代数用法实例解析

x = np.array([[1,2,3],[4,5,6]]) y = np.array([[7,8],[-1,7],[8,9]]) product = x.dot(y) # 或者 product = x @ y 上述代码将计算两个矩阵的乘积，结果为[[29, 49], [71, 121]]。 2. **点积与内积**...

优化这段代码x = np.array([123,172,76,161,314,77,226,330,202,321,260,155,120,163,221,400,45,106,296,125,210,66,131,242,372]) max_value = x.max() min_value = x.min() m = int(math.ceil((max_value - min_value)/w)) bins = [min_value+k*w for k in range(0, m+1)] x_cuts = pd.cut(x, bins, right=False) number = pd.value_counts(x_cuts) array = number.values rows = number.max() width = np.full([m, rows], 0) size = x.size a = 0 for j in range(0, m): for i in range(0, array[j]): width[j][i] = x[a] a = a + 1 mid_width = np.full([m, rows], 0) for i in range(0, m): for j in range(0, array[i]): mid_width[i][j] = np.median(width[i]) print(mid_width)

x = np.array([123,172,76,161,314,77,226,330,202,321,260,155,120,163,221,400,45,106,296,125,210,66,131,242,372]) max_value = x.max() min_value = x.min() m = int(math.ceil((max_value - min_value)/w))...

x = np.array([123,172,76,161,314,77,226,330,202,321,260,155,120,163,221,400,45,106,296,125,210,66,131,242,372]) w = int(input("请输入等宽分箱的宽度：")) max = x.max() min = x.min() m = int(math.ceil((max - min)/w))

这段代码的作用是将给定的一组数据 x 进行等宽分箱处理。其中，w 表示指定的分箱宽度，max 和 min 分别表示 x 中的最大值和最小值，m 表示最终分成的箱子数量。具体实现过程是，根据最大值和最小值以及指定的分箱...

import numpy as np import math x = np.array([123,172,76,161,314,77,226,330,202,321,260,155,120,163,221,400,45,106,296,125,210,66,131,242,372]) x.sort() x=5 max_value = x.max() min_value = x.min() m = int(math.ceil((max_value - min_value)/w)) bins = [min_value+k*w for k in range(0, m+1)] x_cuts = pd.cut(x, bins, right=False) number = pd.value_counts(x_cuts) array = number.values rows = number.max() width = np.full([m, rows], 0) size = x.size a = 0 for j in range(0, m): for i in range(0, array[j]): width[j][i] = x[a] a = a + 1 mid_width = np.full([m, rows], 0) for i in range(0, m): for j in range(0, array[i]): mid_width[i][j] = np.median(width[i]) print("\n中值平滑后的等宽箱：") print(mid_width) 输出结果与预期不符。帮我找出问题

x = np.array([123,172,76,161,314,77,226,330,202,321,260,155,120,163,221,400,45,106,296,125,210,66,131,242,372]) max_value = max(x) min_value = min(x) w = 50 m = int(math.ceil((max_value - min_value) ...

帮我修改一下代码import numpy as npimport mathimport pandas as pdx=np.array([123,68,76,161,52,77,226,350,202,341,260,155,120,163,221,133,45,106,50,125,83,66,131,242,127])x.sort()w=int(input("请输入等深分箱的宽度w："))# 定义等区间bins = [60,70,80,90,100]# 对数据按区间分段，right=False代表区间左闭右开x_cuts = pd.cut(x,bins,right=False)# number数组即为每个区间的数据数量number=pd.value_counts(x_cuts)# 取所有区间中最大的数据数量rows = number.max()# 定义初始化等宽箱widthList = np.full([4,rows],0)size = x.size# 将要处理的数据x按照等宽分箱法分箱i=0for j in range(0,4): for a in range(0,number[j]): widthList[j][a]=x[i] i=i+1print(widthList)

以下是修改后的代码： ...x = np.array([123,68,76,161,52,77,226,350,202,341,260,155,120,163,221,133,45,106,50,125,83,66,131,242,127]) x.sort() w = int(input("请输入等深分箱的宽度w："))

from sklearn.model_selection import train_test_split import sklearn.neural_network as net import matplotlib.cm as cm file_path=r'D:\anaconda3\temp\邮政编码数据.txt' data=pd.read_csv(file_path,header=0) print(data.shape) data.head() X=data.iloc[:,1:-1] Y=data.iloc[:,0] X.shape np.random.seed(1) ids=np.random.choice(len(Y),25) plt.figure(figsize=(8,8)) for i,item in enumerate(ids): img=np.array(X.iloc[item]).reshape(16,16) plt.subplot(5,5,i+1) plt.imshow(img,cmap=cm.gray_r) plt.show X_train,X_test,Y_train,Y_test = train_test_split(X,Y,train_size = 0.60,random_state = 123) nodes = np.arange(1,20,2) acts = ['relu','logistic'] errTrain = np.zeros((len(nodes),2)) errTest = np.zeros((len(nodes),2)) for i,node in enumerate(nodes): for j ,act in enumerate(acts): NeuNet = net.MLPClassifier(hidden_layer_sizes = (node,),activation = act,random_state = 1,max_iter=300) nodes=np.arange(1,20,2) acts=['relu','logistic'] NeuNet.fit(X_train,Y_train) errTrain[i,j] = 1-NeuNet.score(X_train,Y_train) errTest[i,j] = 1-NeuNet.score(X_test,Y_test) plt.plot(nodes,errTest[:,0],label='relu_test',linestyle='-') plt.plot(nodes,errTest[:,1],label='logistic_test',linestyle='-.') plt.plot(nodes,errTrain[:,0],label='relu_train',linestyle='-',linewidth=0.5) plt.plot(nodes,errTrain[:,1],label='logistic_train',linestyle='-',linewidth=0.5) plt.title('2012010812') plt.xlabel('hidden node numbers') plt.ylabel('erros') plt.xticks(nodes) plt.legend()

这段代码看起来有一些问题。...其次，在这个代码块中，没有导入 pandas 库，但是调用了 pandas 中的函数 read_csv。在代码块的后半部分，您定义了变量 nodes，但是在循环中又重新定义了 nodes，这会导致变量被...

import pandas as pd data = pd.read_csv(C:\Users\Administrator\Desktop\pythonsjwj\weibo_senti_100k.csv') data = data.dropna(); data.shape data.head() import jieba data['data_cut'] = data['review'].apply(lambda x: list(jieba.cut(x))) data.head() with open('stopword.txt','r',encoding = 'utf-8') as f: stop = f.readlines() import re stop = [re.sub(' |\n|\ufeff','',r) for r in stop] data['data_after'] = [[i for i in s if i not in stop] for s in data['data_cut']] data.head() w = [] for i in data['data_after']: w.extend(i) num_data = pd.DataFrame(pd.Series(w).value_counts()) num_data['id'] = list(range(1,len(num_data)+1)) a = lambda x:list(num_data['id'][x]) data['vec'] = data['data_after'].apply(a) data.head() from wordcloud import WordCloud import matplotlib.pyplot as plt num_words = [''.join(i) for i in data['data_after']] num_words = ''.join(num_words) num_words= re.sub(' ','',num_words) num = pd.Series(jieba.lcut(num_words)).value_counts() wc_pic = WordCloud(background_color='white',font_path=r'C:\Windows\Fonts\simhei.ttf').fit_words(num) plt.figure(figsize=(10,10)) plt.imshow(wc_pic) plt.axis('off') plt.show() from sklearn.model_selection import train_test_split from keras.preprocessing import sequence maxlen = 128 vec_data = list(sequence.pad_sequences(data['vec'],maxlen=maxlen)) x,xt,y,yt = train_test_split(vec_data,data['label'],test_size = 0.2,random_state = 123) import numpy as np x = np.array(list(x)) y = np.array(list(y)) xt = np.array(list(xt)) yt = np.array(list(yt)) x=x[:2000,:] y=y[:2000] xt=xt[:500,:] yt=yt[:500] from sklearn.svm import SVC clf = SVC(C=1, kernel = 'linear') clf.fit(x,y) from sklearn.metrics import classification_report test_pre = clf.predict(xt) report = classification_report(yt,test_pre) print(report) from keras.optimizers import SGD, RMSprop, Adagrad from keras.utils import np_utils from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.layers.embeddings import Embedding from keras.layers.recurrent import LSTM, GRU model = Sequential() model.add(Embedding(len(num_data['id'])+1,256)) model.add(Dense(32, activation='sigmoid', input_dim=100)) model.add(LSTM(128)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.summary() import matplotlib.pyplot as plt import matplotlib.image as mpimg from keras.utils import plot_model plot_model(model,to_file='Lstm2.png',show_shapes=True) ls = mpimg.imread('Lstm2.png') plt.imshow(ls) plt.axis('off') plt.show() model.compile(loss='binary_crossentropy',optimizer='Adam',metrics=["accuracy"]) model.fit(x,y,validation_data=(x,y),epochs=15)

x, xt, y, yt = train_test_split(vec_data, data['label'], test_size=0.2, random_state=123) # 训练SVM模型 clf = SVC(C=1, kernel='linear') clf.fit(x, y) # 使用测试集进行预测 test_pre = clf.predict(xt) ...

import numpy as np import pandas as pd from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from scipy.spatial.distance import cdist import matplotlib.pyplot as plt from pandas import DataFrame from sklearn.decomposition import PCA plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签 plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 pd.set_option('display.max_rows', None)#显示全部行 pd.set_option('display.max_columns', None)#显示全部列 np.set_printoptions(threshold=np.inf) pd.set_option('display.max_columns', 9000) pd.set_option('display.width', 9000) pd.set_option('display.max_colwidth', 9000) df = pd.read_csv(r'附件1.csv',encoding='gbk') X = np.array(df.iloc[:, 1:]) X=X[0:,1:] k=93 kmeans_model = KMeans(n_clusters=k, random_state=123) fit_kmeans = kmeans_model.fit(X) # 模型训练 #查看聚类结果 kmeans_cc = kmeans_model.cluster_centers_ # 聚类中心 print('各类聚类中心为：\n', kmeans_cc) kmeans_labels = kmeans_model.labels_ # 样本的类别标签 print('各样本的类别标签为：\n', kmeans_labels) r1 = pd.Series(kmeans_model.labels_).value_counts() # 统计不同类别样本的数目 print('最终每个类别的数目为：\n', r1) # 输出聚类分群的结果 # cluster_center = pd.DataFrame(kmeans_model.cluster_centers_, # columns=[ str(x) for x in range(1,94)]) # 将聚类中心放在数据框中 # cluster_center.index = pd.DataFrame(kmeans_model.labels_). \ # drop_duplicates().iloc[:, 0] # 将样本类别作为数据框索引 # print(cluster_center)代码解释

X = np.array(df.iloc[:, 1:]) X=X[0:,1:] 3. 使用KMeans算法进行聚类分析 python k=93 kmeans_model = KMeans(n_clusters=k, random_state=123) fit_kmeans = kmeans_model.fit(X) 4. 输出聚类结果...

Consider a linear model Y = α + β TX + ε. (1) Set X ∼ MV N(0, Σ), Σ = (ρ |i−j| )p×p (the AR(1) structure), where ρ = 0.5, α = 1, β = (2, 1.5, 0, 0, 1, 0, . . . , 0)T , ε ∼ N(0, 1), simulate Y = α + β TX + ε, where the predictor dimension p = 20 and the sample size n = 200. Here, by the model settings, X1, X2 and X5 are the important variables. (2) Estimate regression coefficients using LASSO using the coordinate decent algorithm and soft thresholding . by use 5-folds CV to choose optimal λ by minimizing the CV prediction error (PE), and plot the PE with different λ. python 代码

beta = np.array([2, 1.5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) Sigma = np.zeros((p, p)) for i in range(p): for j in range(p): Sigma[i, j] = rho ** np.abs(i - j) X = np.random....

、分别使用分段线性插值和样条插值预测 P196 例9.4 中的人口数。课本（第三版）P202，习题14 数据：← data=[5.3,7.2,9.6,12.9,17.1,23.2,31.4.4 38.6,50.2,62.9,76.0,92.0,106.5,123.2,...4 131.7,150.7,179.3,204.0,226.5,251.4,275.0,308.71

x = np.array([5.3, 7.2, 9.6, 12.9, 17.1, 23.2, 31.4, 38.6, 50.2, 62.9, 76.0, 92.0, 106.5, 123.2, 131.7, 150.7, 179.3, 204.0, 226.5, 251.4, 275.0, 308.71]) y = np.array([11.2, 13.4, 15.8, 18.7, 22.8, ...

x = [10,13,22,37,45,48,59,65,66,68,68,71,84,88,89,89] y = [19,60,71,74,69,86,146,130,153,144,128,123,127,125]转化为一元线性回归代码

x = np.array([10,13,22,37,45,48,59,65,66,68,68,71,84,88,89,89]).reshape((-1, 1)) y = np.array([19,60,71,74,69,86,146,130,153,144,128,123,127,125]) 接着，我们需要创建一个LinearRegression对象，并...

dbscan聚类结果保存为{"y":123.2885100,"x":42.7373900,"label":8}格式的json文件

X = np.array([[123.2885100, 42.7373900], [123.2885200, 42.7374000], [123.2885300, 42.7373900], [123.2885400, 42.7374000], [123.2885500, 42.7373900], [123.2885600, 42.7374000], [123.2885700, 42....

flask将{"y":123.2885100,"x":42.7373900,"label":8}格式的json文件传给leaflet，写名为dbscan的函数绘制聚类图

X = np.array(X) db = DBSCAN(eps=eps, min_samples=min_samples).fit(X) # 进行聚类 labels = db.labels_ # 聚类后每个点的标签 clusters = {} # 用于存储每个簇的坐标 for i, label in enumerate(labels): ...

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