如何将结果返回：def new_matrix(a,i,j,l): import numpy as np row=a.shape[0] col=a.shape[1] n_m=np.zeros((2*l+row,2*l+col)) n_m[l:l+row,l:l+col]=a f_m=n_m[i:i+2*l+1,j:j+2*l+1]

python 3.74 运行import numpy as np 报错lib\site-packages\numpy\init.py

import numpy as np import os,sys #获取当前文件夹，并根据文件名 def path(fileName): p=sys.path[0]+'\\'+fileName return p #读文件 def readFile(fileName): f=open(path(fileName)) str=f.read() ...

import numpy as np(2).py

翻译这段程序并自行赋值调用：import matplotlib.pyplot as plt import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole grid Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) def sigmoid(x): s = 1/(1+np.exp(-x)) return s def load_planar_dataset(): np.random.seed(1) m = 400 # number of examples N = int(m/2) # number of points per class print(np.random.randn(N)) D = 2 # dimensionality X = np.zeros((m,D)) # data matrix where each row is a single example Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum ray of the flower for j in range(2): ix = range(Nj,N(j+1)) t = np.linspace(j3.12,(j+1)3.12,N) + np.random.randn(N)0.2 # theta r = anp.sin(4t) + np.random.randn(N)0.2 # radius X[ix] = np.c_[rnp.sin(t), rnp.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y def load_extra_datasets(): N = 200 noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) no_structure = np.random.rand(N, 2), np.random.rand(N, 2) return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # 设置最小值和最大值，并给它们一些填充 x_min, x_max = X[0, :].min() - ...

将上述代码放入了Recommenders.py文件中，作为一个自定义工具包。将下列代码中调用scipy包中svd的部分。转为使用Recommenders.py工具包中封装的svd方法。给出修改后的完整代码。import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import * from scipy.sparse.linalg import svds from scipy.sparse import coo_matrix from scipy.sparse import csc_matrix # Load and preprocess data triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd # load dataset triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged['total_listen_count'] # Convert data to sparse matrix format small_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index':'user_index'}, inplace=True) song_codes.rename(columns={'index':'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set,song_codes,how='left') small_set = pd.merge(small_set,user_codes,how='left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float) # Compute SVD def compute_svd(urm, K): U, s, Vt = svds(urm, K) dim = (len(s), len(s)) S = np.zeros(dim, dtype=np.float32) for i in range(0, len(s)): S[i,i] = mt.sqrt(s[i]) U = csc_matrix(U, dtype=np.float32) S = csc_matrix(S, dtype=np.float32) Vt = csc_matrix(Vt, dtype=np.float32) return U, S, Vt def compute_estimated_matrix(urm, U, S, Vt, uTest, K, test): rightTerm = SVt max_recommendation = 10 estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16) recomendRatings = np.zeros(shape=(MAX_UID,max_recommendation ), dtype=np.float16) for userTest in uTest: prod = U[userTest, :]rightTerm estimatedRatings[userTest, :] = prod.todense() recomendRatings[userTest, :] = (-estimatedRatings[userTest, :]).argsort()[:max_recommendation] return recomendRatings K=50 # number of factors urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] U, S, Vt = compute_svd(urm, K) # Compute recommendations for test users # Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True) # Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)

import numpy as np from sklearn.model_selection import train_test_split from Recommenders import SVDRecommender #import the SVDRecommender class from our custom package # Load and preprocess data ...

(143,9)的DataFrame与(143.7)的DataFrame在做以下操作时import numpy as np def GM11(x0): # 灰色预测模型 x1 = np.cumsum(x0) z1 = (x1[:len(x1)-1] + x1[1:])/2.0 z1 = z1.reshape((len(z1),1)) B = np.append(-z1, np.ones_like(z1), axis=1) Y = x0[1:].reshape((len(x0)-1, 1)) [[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Y) return (a, b) def GM11_predict(x0, a, b): # 预测函数 result = [] for i in range(1, 11): result.append((x0[0]-b/a)(1-np.exp(a))np.exp(-a(i-1))) result.append((x0[0]-b/a)(1-np.exp(a))np.exp(-a10)) return result # 计算灰色关联度 def Grey_Relation(x, y): x = np.array(x) y = np.array(y) x0 = x[0] y0 = y[0] x_model = GM11(x) y_model = GM11(y) x_predict = GM11_predict(x, x_model) y_predict = GM11_predict(y, y_model) delta_x = np.abs(x-x_predict)/np.abs(x).max() delta_y = np.abs(y-y_predict)/np.abs(y).max() grey_relation = 0.5np.exp(-0.5((delta_x-delta_y)**2).sum()) return grey_relation # 计算灰色关联度矩阵 def Grey_Relation_Matrix(data1, data2): matrix = [] for i in range(data1.shape[1]): row = [] for j in range(data2.shape[1]): x = data1.iloc[:, i].tolist() y = data2.iloc[:, j].tolist() grey_relation = Grey_Relation(x, y) row.append(grey_relation) matrix.append(row) return np.array(matrix) # 计算人口-经济的灰色关联度矩阵 relation_matrix = Grey_Relation_Matrix(pop_data, eco_data)，发生了以下错误：cannot perform accumulate with flexible type，请写出问题所在，并给出解决代码

这个错误通常是由于数据类型不匹配引起的。其中可能有一列或多列数据类型为...relation_matrix = Grey_Relation_Matrix(pop_data, eco_data) 如果仍然出现错误，请检查数据是否存在缺失或无效值，并进行相应处理。

将着两段代码整合（定义两个函数：一个时高斯消去法、一个是LU分解，另外将矩阵的表示方法由增广矩阵A变成稀系数矩阵和另外一个矩阵）：#Gauss消去法 import numpy as np # 构造增广矩阵 A = np.array([[1, -1, 1, 0], [4, 1, 0, 8], [0, 1, 4, 16]],dtype=float) # 高斯消元过程 n = len(A) for i in range(n): # 主元归一化 if A[i][i] == 0: print("主元为0，无法进行消元") break A[i] = A[i] / A[i][i] for j in range(i + 1, n): A[j] = A[j] - A[i] * A[j][i] # 回代过程 x = np.zeros(n, dtype=float) for i in range(n - 1, -1, -1): x[i] = A[i][-1] - np.dot(A[i][i + 1:-1], x[i + 1:]) print("线性方程组的解为：", x) #LU分解 import numpy as np # 构造增广矩阵 A = np.array([[1, -1, 1, 0], [4, 1, 0, 8], [0, 1, 4, 16]], dtype=float) # LU分解 n = len(A) L = np.zeros((n, n)) U = np.zeros((n, n)) for i in range(n): # 计算L的第i列和U的第i行 for j in range(i, n): U[i][j] = A[i][j] - sum(L[i][k] * U[k][j] for k in range(i)) for j in range(i+1, n): L[j][i] = (A[j][i] - sum(L[j][k] * U[k][i] for k in range(i))) / U[i][i] L[i][i] = 1 # 解方程 b = A[:, -1] y = np.zeros(n) for i in range(n): y[i] = b[i] - sum(L[i][j] * y[j] for j in range(i)) x = np.zeros(n) for i in range(n-1, -1, -1): x[i] = (y[i] - sum(U[i][j] * x[j] for j in range(i+1, n))) / U[i][i] print(x)

L[j][i] = (A[j][i] - sum(L[j][k] * U[k][i] for k in range(i))) / U[i][i] L[i][i] = 1 # 解方程 y = np.zeros(n) for i in range(n): y[i] = b[i] - sum(L[i][j] * y[j] for j in range(i)) x = np....

import numpy as np from py2neo import Graph graph = Graph("http://23/231/23/4:7474/browser/", auth=("x", "xxx!")) # from py2neo import Node, Relationship def load_data(): query = """ MATCH (u:custom)-[]->(p:broadband) RETURN u.number, p.name, 1 """ result = graph.run(query) # 构建用户商品矩阵 users = set() products = set() data = [] for row in result: user_id = row[0] product_id = row[1] quantity = row[2] users.add(user_id) products.add(product_id) data.append((user_id, product_id, quantity)) # 构建两个字典user_index，user_index，key为名称，value为排序的0~N-1的序号 user_index = {u: i for i, u in enumerate(users)} print("user_index:",user_index) product_index = {p: i for i, p in enumerate(products)} print("product_index:",product_index) # 构建全零矩阵 np.zeros matrix = np.zeros((len(users), len(products))) # 将存在关系的节点在矩阵中用值1表示 quantity = 1 for user_id, product_id, quantity in data: matrix[user_index[user_id], product_index[product_id]] = quantity # print("matrix:",matrix) # user_names = list(user_index.keys()) # product_names = list(product_index.keys()) # print("user_names:", user_names) # print("product_names:", product_names) # 转成用户商品矩阵 # matrix 与 np.mat转化后格式内容一样 user_product_matrix = np.mat(matrix) # print(user_product_matrix) return user_product_matrix def generate_dict(dataTmp): m,n = np.shape(dataTmp) print(m,n) data_dict = {} for i in range(m): tmp_dict = {} # 遍历矩阵，对每一行进行遍历，找到每行中的值为1 的列进行输出 for j in range(n): if dataTmp[i,j] != 0: tmp_dict["D_"+str(j)] = dataTmp[i,j] print(str(j)) print(tmp_dict["D_"+str(j)]) data_dict["U_"+str(i)] = tmp_dict print(tmp_dict) print(str(i)) for j in range(n): tmp_dict = {} for i in range(m): if dataTmp[i,j] != 0: tmp_dict["U_"+str(i)] = dataTmp[i,j] data_dict["D_"+str(j)] = tmp_dict return data_dict def PersonalRank(data_dict,alpha,user,maxCycles): rank = {} for x in data_dict.keys(): rank[x] = 0 rank[user] = 1 step = 0 while step < maxCycles: tmp = {} for x in data_dict.keys(): tmp[x] = 0 for i ,ri in data_dict.items(): for j in ri.keys(): if j not in tmp: tmp[j] = 0 tmp[j] += alpha+rank[i] / (1.0*len(ri)) if j == user: tmp[j] += (1-alpha) check = [] for k in tmp.keys(): check.append(tmp[k] - rank[k]) if sum(check) <= 0.0001: break rank = tmp if step % 20 == 0: print("iter:",step) step = step + 1 return rank def recommand(data_dict,rank,user): items_dict = {} items = [] for k in data_dict[user].keys(): items.append(k) for k in rank.keys(): if k.startswith("D_"): if k not in items: items_dict[k] = rank[k] result = sorted(items_dict.items(),key=lambda d:d[1],reverse=True) return result print("-------------") data_mat = load_data() print("-------------") data_dict = generate_dict(data_mat) print("-------------") rank = PersonalRank(data_dict,0.85,"U_1",500) print("-------------") result = recommand(data_dict,rank,"U_1") print(result) 优化这段代码，将U_N替换成U_NUMBER D_N替换成D_NAME

import numpy as np from py2neo import Graph graph = Graph("http://23/231/23/4:7474/browser/", auth=("x", "xxx!")) def load_data(): query = """ MATCH (u:custom)-[]->(p:broadband) RETURN u.number...

the element in labels should strictly be 1 or 0, and it must be 1 in sum of a row, Continue to refine the last code and don't add any other packages but numpy.

Here's a refined version of the code that ensures that the elements in the labels array are either 0 or 1 and that the sum of each row is 1: import numpy as np def label_propagation(xs: np....

Please write a code in Python to calculate the composition of fuzzy matrix

import numpy as np # Define two fuzzy matrices A and B A = np.array([[0.8, 0.2], [0.4, 0.6]]) B = np.array([[0.7, 0.3], [0.5, 0.5]]) # Define a function to calculate the composition of two fuzzy ...

用numpy写一个匈牙利算法

import numpy as np def hungarian_algorithm(cost_matrix): # 初始化 num_rows, num_cols = cost_matrix.shape labels = np.zeros((num_rows, num_cols)) matching = np.full(num_cols, -1, dtype=int) slack...

编写一个函数，实现将numpy矩阵的每一行元素都减去该行的平均值。

import numpy as np def subtract_row_mean(matrix): row_means = np.mean(matrix, axis=1, keepdims=True) return np.subtract(matrix, np.tile(row_means, (1, matrix.shape[1]))) 其中，axis=1表示对每一...

用python完成1.输入一个稀疏矩阵。 2、有效性判断。 3、稀疏数字占比是否低于稀疏比率判断。 4、打印稀疏矩阵和压缩后的结果

import numpy as np def input_sparse_matrix(): row, col = map(int, input("请输入稀疏矩阵的行列数，用空格分隔：").split()) matrix = np.zeros((row, col)) for i in range(row): row_data = input("请...

如何将结果返回：def new_matrix(a,i,j,l): import numpy as np row=a.shape[0] col=a.shape[1] n_m=np.zeros((2l+row,2l+col)) n_m[l:l+row,l:l+col]=a f_m=n_m[i:i+2l+1,j:j+2l+1]

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如何将结果返回：def new_matrix(a,i,j,l): import numpy as np row=a.shape[0] col=a.shape[1] n_m=np.zeros((2l+row,2l+col)) n_m[l:l+row,l:l+col]=a f_m=n_m[i:i+2l+1,j:j+2l+1]

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