.csv数据中输入固定的房间面积为60，朝向中1代表房屋东，0代表房屋西，2代表房屋南，3代表房屋北，日照时长，室外最高温，最低温，室内温度，室内人数作为输入，一般舒适度为输出，利用灰狼优化算法进行预测，将一般舒适度与实际的舒适度做比较，将结果绘图进行数据可视化。 python

好的，我了解了您的问题。这是一个回归问题，您可以使用灰狼优化算法进行预测。以下是一些代码示例，需要使用pandas、numpy、sklearn、matplotlib和h5py库，您可以使用pip命令进行安装。

首先，我们需要加载数据集，并将其分为训练集和测试集。

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

然后，我们需要将数据规范化。这可以通过将每个特征缩放到0和1之间来完成。

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

接下来，我们可以使用灰狼优化算法来训练模型。

import math
import random
import numpy as np
import matplotlib.pyplot as plt

def init(positions, dim):
    for i in range(positions.shape[0]):
        for j in range(dim):
            positions[i][j] = np.random.uniform(-1, 1)
    return positions

def get_fitness(X_train, y_train, X_test, y_test, solution):
    clf = KNeighborsRegressor(n_neighbors=5)
    clf.fit(X_train, y_train)
    y_pred_test = clf.predict(X_test)
    error = np.mean((y_test - y_pred_test) ** 2)
    fitness = 1 / (1 + error)
    return fitness

def update_positions(positions, alpha, beta, delta, dim):
    for i in range(positions.shape[0]):
        for j in range(dim):
            r1, r2 = random.uniform(0, 1), random.uniform(0, 1)
            A1, A2 = 2 * alpha * r1 - alpha, 2 * alpha * r2 - alpha
            C1, C2 = 2 * r1, 2 * r2
            D_alpha, D_beta, D_delta = abs(C1 * delta[j] - positions[i][j]), abs(C2 * delta[j] - positions[i][j]), abs(
                C1 * delta[j] - C2 * delta[j])
            X1, X2, X3 = delta[j] - A1 * D_alpha, delta[j] - A2 * D_beta, delta[j] - alpha * D_delta
            positions[i][j] = (X1 + X2 + X3) / 3
    return positions

def GWO(X_train, y_train, X_test, y_test, num_iter, num_search_agents=5):
    dim = X_train.shape[1]
    alpha, beta, delta = np.zeros(dim), np.zeros(dim), np.zeros(dim)
    positions = np.zeros((num_search_agents, dim))
    positions = init(positions, dim)
    fitness = np.zeros(num_search_agents)
    alpha_score = float('-inf')
    alpha_pos = np.zeros(dim)
    for i in range(num_iter):
        for j in range(num_search_agents):
            fitness[j] = get_fitness(X_train, y_train, X_test, y_test, positions[j])
            if fitness[j] > alpha_score:
                alpha_score = fitness[j]
                alpha_pos = positions[j]
        for j in range(num_search_agents):
            if fitness[j] < alpha_score and fitness[j] >= np.max(fitness[:j]):
                alpha_pos = positions[j]
                alpha_score = fitness[j]
            if fitness[j] < beta_score and fitness[j] >= np.max(fitness[:j]):
                beta_pos = positions[j]
                beta_score = fitness[j]
            if fitness[j] < delta_score and fitness[j] >= np.max(fitness[:j]):
                delta_pos = positions[j]
                delta_score = fitness[j]
        alpha, beta, delta = 2 * alpha - (beta + delta), 2 * beta - (alpha + delta), 2 * delta - (alpha + beta)
        positions = update_positions(positions, alpha, beta, delta, dim)
    return alpha_pos

最后，我们可以使用matplotlib库将结果可视化。

from sklearn.neighbors import KNeighborsRegressor

alpha_pos = GWO(X_train, y_train, X_test, y_test, num_iter=100)

clf = KNeighborsRegressor(n_neighbors=5)
clf.fit(X_train, y_train)
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)

plt.plot(y_test, label='True')
plt.plot(y_pred_test, label='Predicted')
plt.legend()
plt.show()

这将显示一个包含实际值和预测值的折线图。