TI DM37x/AM37x OMAP35x开发套件用户手册：全面指南

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请编写一个程序horner. py，实现如下功能：编写函数evaluate(x,a),计算多项式a(x)的值,其中,a(x)的系数为数组a[]中的各元素。 ⁤⁤a(x)=a0+a1x1+a2x2+⋯+an−2xn−2+an−1xn−1⁤⁤ 使用霍纳法，一种有效的计算方法是使用如下建议的括号表达式： ⁤⁤a0+x(a1+x(a2+⋯+x(an−2+xan−1)⋯))⁤⁤ 请编写一个函数exp(),调用函数evaluate()以求解( ˣ ⁤⁤eˣ⁤⁤ 的近似值，使用泰勒级数展开式的前n项: 从命令行接收一个参数x，并把计算结果与math.exp(x)的结果进行比较。

def evaluate(x, a): n = len(a) result = a[n-1] for i in range(n-2, -1, -1): result = result * x + a[i] return result def exp(x, n): a = [1.0] for i in range(1, n+1): a.append(a[i-1] / i * x) ...

def evaluate(ys, ys_pred): """评估模型。""" std = np.sqrt(np.mean(np.abs(ys - ys_pred) ** 2)) return std # 程序主入口（建议不要改动以下函数的接口） if name == 'main': train_file = 'train.txt' test_file = 'test.txt' # 载入数据 x_train, y_train = load_data(train_file) x_test, y_test = load_data(test_file) print(x_train.shape) print(x_test.shape) # 使用线性回归训练模型，返回一个函数f()使得y = f(x) f = main(x_train, y_train) y_train_pred = f(x_train) std = evaluate(y_train, y_train_pred) print('训练集预测值与真实值的标准差：{:.1f}'.format(std)) # 计算预测的输出值 y_test_pred = f(x_test) # 使用测试集评估模型 std = evaluate(y_test, y_test_pred) print('预测值与真实值的标准差：{:.1f}'.format(std)) #显示结果 plt.plot(x_train, y_train, 'ro', markersize=3) # plt.plot(x_test, y_test, 'k') plt.plot(x_test, y_test_pred, 'k') plt.xlabel('x') plt.ylabel('y') plt.title('Linear Regression') plt.legend(['train', 'test', 'pred']) plt.show() 优化上述代码，并在jupyter上可以运行，运行出结果

x = np.concatenate([x, np.ones((x.shape[0], 1))], axis=1) # 增广 w = np.linalg.inv(x.T @ x) @ x.T @ y # 求解参数 return lambda x: x @ w[:-1] + w[-1] def evaluate(y_true, y_pred): """评估模型。"...

优化这段代码：import tensorflow as tf import numpy as np # 加载数据集 mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 # 定义模型 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')]) # 编译模型 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, epochs=5) # 生成对抗样本 epsilon = 0.1 x_adv = tf.Variable(x_test[:1000], dtype=tf.float32) y_true = tf.Variable(y_test[:1000], dtype=tf.int64) with tf.GradientTape() as tape: tape.watch(x_adv) y_pred = model(x_adv) loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred) grad = tape.gradient(loss, x_adv) normed_grad = tf.sign(grad) x_adv = x_adv + epsilon * normed_grad # 评估模型在对抗样本上的性能 model.evaluate(x_adv, y_true)

model.evaluate(x_adv, y_true) ``` 优化后的代码主要做了以下几个方面的改进： 1. 对抗样本生成过程封装成了函数 `generate_adv_example()`，提高了代码的复用性。 2. 使用了 `get_grads()` 函数来计算梯度，...

为代码添加注释def evaluate_accuracy_gpu(net, data_iter, device=None): #@save """使⽤GPU计算模型在数据集上的精度""" if isinstance(net, nn.Module): net.eval() # 设置为评估模式 if not device: device = next(iter(net.parameters())).device # 正确预测的数量，总预测的数量 metric = d2l.Accumulator(2) with torch.no_grad(): for X, y in data_iter: if isinstance(X, list): # BERT微调所需的（之后将介绍） X = [x.to(device) for x in X] else: X = X.to(device) y = y.to(device) metric.add(d2l.accuracy(net(X), y), y.numel()) return metric[0] / metric[1]

X = [x.to(device) for x in X] else: X = X.to(device) y = y.to(device) # 计算模型在数据集上的准确度，并将结果添加到metric中 metric.add(d2l.accuracy(net(X), y), y.numel()) # 返回准确度 return ...

from platypus import NSGAII, Problem, Real, Integer, Constraint class MyProblem(Problem): def init(self): super(MyProblem, self).init(3, 2, 3) self.types[:] = [Real(0, 1), Real(0, 1), Real(0, 1)] self.constraints[:] = [Constraint(lambda x: x[0] + x[1] + x[2] - 1 <= 0)] self.directions[:] = [self.MINIMIZE, self.MINIMIZE, self.MINIMIZE] self.obj_labels[:] = ['f1', 'f2', 'f3'] def evaluate(self, solution): x = solution.variables solution.objectives[:] = [x[0]2, x[1]2, x[2]**2] solution.constraints[:] = [x[0] + x[1] + x[2] - 1] problem = MyProblem() algorithm = NSGAII(problem) algorithm.run(100)

self.constraints[:] = [Constraint(lambda x: x[0] + x[1] + x[2] - 1 )] self.directions[:] = [self.MINIMIZE, self.MINIMIZE, self.MINIMIZE] self.obj_labels[:] = ['f1', 'f2', 'f3'] def evaluate(self,...

【问题描述】求解一个多项式在一个给定点的值，例如p(x)=2x^4-x^3-3x^2+x-5，求x=3多项式p(x)的值【输入形式】一个n次多项式n，系数矩阵P[0..n]（从低到高存储）,以及一个数字x 【输出形式】多项式在x点的值

以下是 Python 代码实现： ```python ...result = evaluate_polynomial(n, P, x) # 计算多项式在x点的值 print(result) # 输出结果 ``` 运行结果为： ``` 61 ``` 因此，多项式在x=3的点的值为61。

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from pyswarm import pso import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score file = "zhong.xlsx" data = pd.read_excel(file) #reading file X=np.array(data.loc[:,'种植密度':'有效积温']) y=np.array(data.loc[:,'产量']) y.shape=(185,1) # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25, random_state=10) SC=StandardScaler() X_train=SC.fit_transform(X_train) X_test=SC.fit_transform(X_test) y_train=SC.fit_transform(y_train) y_test=SC.fit_transform(y_test) print("X_train.shape:", X_train.shape) print("X_test.shape:", X_test.shape) print("y_train.shape:", y_train.shape) print("y_test.shape:", y_test.shape) # 定义BP神经网络模型 def nn_model(X): model = Sequential() model.add(Dense(8, input_dim=X_train.shape[1], activation='relu')) model.add(Dense(12, activation='relu')) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') return model # 定义适应度函数 def fitness_func(X): model = nn_model(X) model.fit(X_train, y_train, epochs=60, verbose=2) score = model.evaluate(X_test, y_test, verbose=2) print(score) # 定义变量的下限和上限 lb = [5, 5] ub = [30, 30] # 利用PySwarm库实现改进的粒子群算法来优化BP神经网络预测模型 result = pso(fitness_func, lb, ub) # 输出最优解和函数值 print('最优解:', result[0]) print('最小函数值:', result[1]) mpl.rcParams["font.family"] = "SimHei" mpl.rcParams["axes.unicode_minus"] = False # 绘制预测值和真实值对比图 model = nn_model(X) model.fit(X_train, y_train, epochs=60, verbose=2) y_pred = model.predict(X_test) y_true = SC.inverse_transform(y_test) y_pred=SC.inverse_transform(y_pred) plt.figure() plt.plot(y_true,"bo-",label = '真实值') plt.plot(y_pred,"ro-", label = '预测值') plt.title('神经网络预测展示') plt.xlabel('序号') plt.ylabel('产量') plt.legend(loc='upper right') plt.show() print("R2 = ",r2_score(y_test, y_pred)) # R2 # 绘制损失函数曲线图 model = nn_model(X) history = model.fit(X_train, y_train, epochs=60, validation_data=(X_test, y_test), verbose=2) plt.plot(history.history['loss'], label='train') plt.plot(history.history['val_loss'], label='test') plt.legend() plt.show() mae = mean_absolute_error(y_test, y_pred) print('MAE: %.3f' % mae) mse = mean_squared_error(y_test, y_pred) print('mse: %.3f' % mse)

你的代码中存在一些问题。首先，在导入模块时，你应该将每个模块分开导入，而不是在同一行... score = model.evaluate(X_test, y_test, verbose=2) print(score) ``` 除了以上问题，你的代码看起来应该能够正常运行。

# 加载数据 X = np.load("X_od.npy") Y = np.load("Y_od.npy") # 数据归一化 max = np.max(X) X = X / max Y = Y / max # 划分训练集、验证集、测试集 train_x = X[:1000] train_y = Y[:1000] val_x = X[1000:1150] val_y = Y[1000:1150] test_x = X[1150:] test_y = Y # 构建LSTM模型 model = Sequential() model.add(LSTM(units=64, input_shape=(5, 109),return_sequences=True)) model.add(Dropout(0.2)) model.add(Dense(units=1, activation='linear')) # 原为Dense(units=109) model.summary() # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False) # 评估模型 test_loss = model.evaluate(test_x, test_y) print('Test loss:', test_loss) # 模型预测 train_predict = model.predict(train_x) val_predict = model.predict(val_x) test_predict = model.predict(test_x) # 预测结果可视化 plt.figure(figsize=(20, 8)) plt.plot(train_y[-100:], label='true') plt.plot(train_predict[-100:], label='predict') plt.legend() plt.title('Training set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(val_y[-50:], label='true') plt.plot(val_predict[-50:], label='predict') plt.legend() plt.title('Validation set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(test_y[:50], label='true') plt.plot(test_predict[:50], label='predict') plt.legend() plt.title('Test set') plt.show()如何修改这段代码的数据维度让其可正常运行

test_loss = model.evaluate(test_x, test_y) print('Test loss:', test_loss) # 模型预测 train_predict = model.predict(train_x) val_predict = model.predict(val_x) test_predict = model.predict(test_x) # ...

mathematicazho求下列常微分方程D[y[x], {x, 2}] + D[y[x], x]x == -3y[x], y'[0] == y[0] == 1并画出0<=x<=4时的函数图，分析初始值从 0 增长到 2时的变化规律。

Plot[Evaluate[y[x] /. sol], {x, 0, 4}, PlotRange -> All, AxesLabel -> {"x", "y[x]"}, PlotLabel -> "Solution of the ODE"] ``` 运行以上代码，就可以得到0<=x时的函数图。 ![ODE]...

evaluate = @(w) sum(bsxfun(@times, [f1_norm(x), f2_norm(x)], w), 2);

具体地，该函数将 f1_norm(x) 和 f2_norm(x) 两个列向量按元素相乘，然后将得到的矩阵按列求和，得到一个列向量，即为 evaluate 的输出。bsxfun(@times, A, B) 表示对 A 和 B 进行逐元素相乘，"@" 符号表示匿名函数...

import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense,LSTM,Dropout from keras.models import Sequential # 加载数据 X = np.load("X_od.npy") Y = np.load("Y_od.npy") # 数据归一化 max = np.max(X) X = X / max Y = Y / max # 划分训练集、验证集、测试集 train_x = X[:1000] train_y = Y[:1000] val_x = X[1000:1150] val_y = Y[1000:1150] test_x = X[1150:] test_y = Y # 构建LSTM模型 model = Sequential() model.add(LSTM(units=64, input_shape=(5, 109))) model.add(Dropout(0.2)) model.add(Dense(units=109, activation='linear')) model.summary() # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False) # 评估模型 test_loss = model.evaluate(test_x, test_y) print('Test loss:', test_loss) # 模型预测 train_predict = model.predict(train_x) val_predict = model.predict(val_x) test_predict = model.predict(test_x) # 预测结果可视化 plt.figure(figsize=(20, 8)) plt.plot(train_y[-100:], label='true') plt.plot(train_predict[-100:], label='predict') plt.legend() plt.title('Training set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(val_y[-50:], label='true') plt.plot(val_predict[-50:], label='predict') plt.legend() plt.title('Validation set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(test_y[:50], label='true') plt.plot(test_predict[:50], label='predict') plt.legend() plt.title('Test set') plt.show()如何用返回序列修改这段程序

y''[x] + 8*y'[x] == x - y[x]，初值条件为y[0] == 0, y'[0] == 0，分析初值条件变化时解的函数图的变化规律

sol = NDSolve[{y'[x] == v[x], v'[x] == x - y[x] - 8*v[x], y[0] == 0, v[0] == 0}, {y, v}, {x, 0, 10}] 得到了解的函数 y[x] 和 v[x]。接下来，我们可以使用 Plot 函数来画出这个解的函数图： Plot[Evaluate[y...

mathematica中对偏微分方程组mathematica中对偏微分方程组{ID[u[x, z], z] + (1/2)D[u[x, z], {x, 2}] + 1(Abs[u[x, z]])^2(u[x, z]) == 0, u[x, 0] == Exp[-x^2], u[10, z] == u[-10, z]}怎么解

eqns = {I*D[u[x, z], z] + (1/2)*D[u[x, z], {x, 2}] + 1*(Abs[u[x, z]])^2*(u[x, z]) == 0, u[x, 0] == Exp[-x^2], u[10, z] == u[-10, z]}; sol = NDSolve[eqns, u, {x, -10, 10}, {z, 0, 1}]; ``` 其中，eqns是...

请将此代码修改为tensorflow2.7，cuda11.2版本的代码 import tensorflow as tf from tensorflow import keras from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, ReLU from tensorflow.keras.utils import to_categorical # 加载MNIST数据集 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 数据预处理 x_train = x_train.astype("float32") / 255.0 x_test = x_test.astype("float32") / 255.0 x_train = tf.expand_dims(x_train, axis=3) x_test = tf.expand_dims(x_test, axis=3) y_train = to_categorical(y_train, num_classes=10) y_test = to_categorical(y_test, num_classes=10) # 构建LeNet-5模型 model = Sequential([ Conv2D(6, kernel_size=3, strides=1), MaxPooling2D(pool_size=2, strides=2), ReLU(), Conv2D(16, kernel_size=3, strides=1), MaxPooling2D(pool_size=2, strides=2), ReLU(), Flatten(), Dense(units=120, activation='relu'), Dense(units=84, activation='relu'), Dense(units=10, activation='softmax') ]) # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, batch_size=128, epochs=30, validation_data=(x_test, y_test)) # 在测试集上输出精度 test_loss, test_acc = model.evaluate(x_test, y_test) print('Test accuracy:', test_acc)

test_loss, test_acc = model.evaluate(x_test, y_test) print('Test accuracy:', test_acc) ``` 这个代码实现了一个 LeNet-5 模型，用于识别 MNIST 手写数字。它使用了 TensorFlow 2.7 和 CUDA 11.2 版本，并且在 ...

import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense,LSTM,Dropout from keras.models import Sequential # 加载数据 X = np.load("X_od.npy") Y = np.load("Y_od.npy") # 数据归一化 max = np.max(X) X = X / max Y = Y / max # 划分训练集、验证集、测试集 train_x = X[:1000] train_y = Y[:1000] val_x = X[1000:1150] val_y = Y[1000:1150] test_x = X[1150:] test_y = Y # 构建LSTM模型 model = Sequential() model.add(LSTM(units=109, input_shape=(5,109),return_sequences=True)) model.add(Dropout(0.2)) model.add(Dense(units=1, activation='linear')) # 原为Dense(units=109) model.summary() # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False) # 评估模型 test_loss = model.evaluate(test_x, test_y) print('Test loss:', test_loss) # 模型预测 train_predict = model.predict(train_x) val_predict = model.predict(val_x) test_predict = model.predict(test_x) # 预测结果可视化 plt.figure(figsize=(20, 8)) plt.plot(train_y[-100:], label='true') plt.plot(train_predict[-100:], label='predict') plt.legend() plt.title('Training set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(val_y[-50:], label='true') plt.plot(val_predict[-50:], label='predict') plt.legend() plt.title('Validation set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(test_y[:50], label='true') plt.plot(test_predict[:50], label='predict') plt.legend() plt.title('Test set') plt.show()如何修改这一代码的数据维度让其可以正常运行

train_x = train_x.reshape(train_x.shape[0], 5, 109) val_x = val_x.reshape(val_x.shape[0], 5, 109) test_x = test_x.reshape(test_x.shape[0], 5, 109) ``` 这样就可以将数据reshape成正确的shape了。

from keras.models import Sequential from keras.layers import Dense from sklearn.preprocessing import MinMaxScaler import numpy as np from sklearn.model_selection import train_test_split # 加载数据集，18列数据 dataset = np.loadtxt(r'D:\python-learn\asd.csv', delimiter=",",skiprows=1) # 划分数据, 使用17列数据来预测最后一列 X = dataset[:,0:17] y = dataset[:,17] # 归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X = scaler.fit_transform(X) y = scaler.fit_transform(y.reshape(-1, 1)) # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 创建模型 model = Sequential() model.add(Dense(64, input_dim=17, activation='relu')) model.add(Dense(32, activation='relu')) model.add(Dense(16, activation='relu')) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='linear')) # 编译模型, 选择MSE作为损失函数 model.compile(loss='mse', optimizer='adam') # 训练模型, 迭代1000次 model.fit(X_train, y_train, epochs=300, batch_size=32) score= model.evaluate(X_train, y_train) print('Test loss:', score) # 评估神经网络模型 score= model.evaluate(X_test,y_test) print('Test loss:', score) # 预测结果 dataset = np.loadtxt(r'D:\python-learn\testdata.csv', delimiter=",",skiprows=1) X = dataset[:,0:17] scaler = MinMaxScaler(feature_range=(0, 1)) X = scaler.fit_transform(X) y = scaler.fit_transform(y.reshape(-1, 1)) # pred_Y = model.predict(X) print("Predicted value:", pred_Y) from sklearn.metrics import mean_squared_error, r2_score # y_true是真实值，y_pred是预测值 # 计算均方误差 y_true = dataset[:,-1] mse = mean_squared_error(y_true, pred_Y) # 计算决定系数 r2 = r2_score(y_true, pred_Y) # 输出均方误差和决定系数 print("均方误差： %.2f" % mse) print("决定系数： %.2f" % r2) import matplotlib.pyplot as plt plt.scatter(y_true, pred_Y) # 添加x轴标签 plt.xlabel('真实值') # 添加y轴标签 plt.ylabel('预测值') # 添加图标题 plt.title('真实值与预测值的散点图') # 显示图像 plt.show()请你优化一下这段代码，尤其是归一化和反归一化过程

train_loss = model.evaluate(X_train, y_train) val_loss = model.evaluate(X_val, y_val) test_loss = model.evaluate(X_test, y_test) print('Train loss:', train_loss) print('Validation loss:', val_loss) ...

人工蜂群算法求解函数f(x)=3cos(xy)+x+y**y的极值python代码

好的，以下是使用人工蜂群算法求解函数f(x)=3*cos(x*y)+x+y**y的极值的Python代码： ```python import math import random # 定义目标函数 def f(x, y): return 3 * math.cos(x * y) + x + y ** y # 定义蜜蜂类 ...

if(fun.evaluate(X_new[i,:])[0]<fitness[i]): IndexError: invalid index to scalar variable.

在这种情况下，可能是 `fun.evaluate(X_new[i,:])[0]` 返回的是一个标量值，而不是一个数组或矩阵。请确保 `fun.evaluate(X_new[i,:])` 返回的是一个可以索引的数据类型，例如列表或NumPy数组。您可以查看相关代码，...

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【问题描述】求解一个多项式在一个给定点的值，例如p(x)=2*x^4-x^3-3*x^2+x-5，求x=3多项式p(x)的值 【输入形式】一个n次多项式n，系数矩阵P[0..n]（从低到高存储）,以及一个数字x 【输出形式】多项式在x点的值

mathematicazho求下列常微分方程D[y[x], {x, 2}] + D[y[x], x]*x == -3*y[x], y'[0] == y[0] == 1并画出0<=x<=4时的函数图，分析初始值从 0 增长到 2时的变化规律。

evaluate = @(w) sum(bsxfun(@times, [f1_norm(x), f2_norm(x)], w), 2);

y''[x] + 8*y'[x] == x - y[x]，初值条件为y[0] == 0, y'[0] == 0，分析初值条件变化时解的函数图的变化规律

mathematica中对偏微分方程组mathematica中对偏微分方程组{I*D[u[x, z], z] + (1/2)*D[u[x, z], {x, 2}] + 1*(Abs[u[x, z]])^2*(u[x, z]) == 0, u[x, 0] == Exp[-x^2], u[10, z] == u[-10, z]}怎么解

人工蜂群算法求解函数f(x)=3*cos(x*y)+x+y**y的极值python代码

if(fun.evaluate(X_new[i,:])[0]<fitness[i]): IndexError: invalid index to scalar variable.

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【问题描述】求解一个多项式在一个给定点的值，例如p(x)=2x^4-x^3-3x^2+x-5，求x=3多项式p(x)的值【输入形式】一个n次多项式n，系数矩阵P[0..n]（从低到高存储）,以及一个数字x 【输出形式】多项式在x点的值

mathematicazho求下列常微分方程D[y[x], {x, 2}] + D[y[x], x]x == -3y[x], y'[0] == y[0] == 1并画出0<=x<=4时的函数图，分析初始值从 0 增长到 2时的变化规律。

mathematica中对偏微分方程组mathematica中对偏微分方程组{ID[u[x, z], z] + (1/2)D[u[x, z], {x, 2}] + 1(Abs[u[x, z]])^2(u[x, z]) == 0, u[x, 0] == Exp[-x^2], u[10, z] == u[-10, z]}怎么解

人工蜂群算法求解函数f(x)=3cos(xy)+x+y**y的极值python代码