q = p.cumsum()解释代码
时间: 2024-06-07 07:06:33 浏览: 18
这段代码中,p是一个一维数组,cumsum()是numpy中的函数,用于计算数组中元素的累积和。具体来说,p.cumsum()返回一个新的数组q,其中q[i]等于p[0]+p[1]+...+p[i],表示从数组p的第一个元素开始到第i个元素的累积和。例如,如果p=[1,2,3,4],则q=[1,3,6,10],因为q[0]=p[0]=1,q[1]=p[0]+p[1]=1+2=3,q[2]=p[0]+p[1]+p[2]=1+2+3=6,q[3]=p[0]+p[1]+p[2]+p[3]=1+2+3+4=10。
相关问题
import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error import pymysql import time,os import re import requests import urllib from datetime import datetime # ---------连接-------------- connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8') #服务器名,账户,密码,数据库名称 cur = connect.cursor() print(cur) # 读取数据 try: select_sqli = "SELECT time,xiaoliang FROM sheji.sale where chexing='海豚';" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") # 转换时间格式 data['time'] = pd.to_datetime(data['time'], format='%Y-%m') print(data)这个代码继续怎么写
接下来可以对数据进行预处理和可视化分析,例如:
1. 将时间列设置为数据的索引
```
data = data.set_index('time')
```
2. 对销量数据进行差分,以便将其转换为平稳的时间序列
```
diff_data = data.diff().dropna()
```
3. 对平稳时间序列进行自相关和偏自相关分析,以确定 ARIMA 模型的参数
```
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
plot_acf(diff_data)
plot_pacf(diff_data)
```
4. 通过交叉验证选择最佳的 ARIMA 模型
```
from sklearn.model_selection import TimeSeriesSplit
from itertools import product
p = range(0, 3)
d = range(0, 3)
q = range(0, 3)
params = product(p, d, q)
params_list = list(params)
tss = TimeSeriesSplit(n_splits=3)
best_model = None
best_mse = np.inf
for params in params_list:
mse_list = []
for train_index, test_index in tss.split(diff_data):
train_data = diff_data.iloc[train_index]
test_data = diff_data.iloc[test_index]
try:
model = ARIMA(train_data, order=params)
model_fit = model.fit(disp=False)
pred = model_fit.forecast(len(test_data))[0]
mse = mean_squared_error(test_data, pred)
mse_list.append(mse)
except:
continue
if len(mse_list) > 0:
mean_mse = np.mean(mse_list)
if mean_mse < best_mse:
best_mse = mean_mse
best_model = params
print(best_model)
```
5. 使用最佳的 ARIMA 模型进行预测,并将结果可视化
```
model = ARIMA(diff_data, order=best_model)
model_fit = model.fit(disp=False)
pred = model_fit.forecast(12)[0]
pred_cumsum = pred.cumsum()
forecast_data = pd.Series(data.iloc[-1, 0], index=pd.date_range(start=data.index[-1], periods=12, freq='M'))
forecast_data += pred_cumsum
plt.plot(data)
plt.plot(forecast_data)
```
利用蚁群优化算法实现支持向量机的二类分类问题python代码
以下是利用蚁群优化算法实现支持向量机的二类分类问题的 Python 代码:
```python
import numpy as np
from sklearn.svm import SVC
# 定义蚂蚁类
class Ant():
def __init__(self, w, b, x, y, c, kernel):
self.w = w
self.b = b
self.x = x
self.y = y
self.c = c
self.kernel = kernel
self.alpha = np.zeros_like(y)
self.fitness = None
# 计算适应度函数值
def calculate_fitness(self):
clf = SVC(C=self.c, kernel=self.kernel)
clf.fit(self.x, self.y)
self.alpha = clf.dual_coef_.ravel()
self.b = clf.intercept_
self.fitness = 1.0 / clf.score(self.x, self.y)
# 更新权重向量
def update_weight(self):
delta_w = np.dot(self.alpha * self.y, self.x)
self.w = self.w + delta_w
# 更新偏置项
def update_bias(self):
self.b = self.b + np.mean(self.y - np.dot(self.x, self.w))
# 更新蚂蚁的位置
def update_position(self, rho, q0, pheromone, alpha, beta):
n = self.x.shape[0]
p = np.zeros(n)
for i in range(n):
p[i] = pheromone[self.w.dot(self.x[i]) + self.b]
p = p ** alpha
p = p / np.sum(p)
if np.random.rand() < q0:
j = np.argmax(p)
else:
cumsum_p = np.cumsum(p)
cumsum_p = cumsum_p / cumsum_p[-1]
j = np.searchsorted(cumsum_p, np.random.rand())
self.alpha[j] = self.alpha[j] + rho * (1 - self.fitness) ** beta
# 蚂蚁进行一次迭代
def step(self, rho, q0, pheromone, alpha, beta):
self.calculate_fitness()
self.update_weight()
self.update_bias()
self.update_position(rho, q0, pheromone, alpha, beta)
# 定义蚁群类
class AntColony():
def __init__(self, x, y, c, kernel='linear', alpha=1.0, beta=1.0, rho=0.1, q0=0.8, num_ants=10, num_iterations=100):
self.x = x
self.y = y
self.c = c
self.kernel = kernel
self.alpha = alpha
self.beta = beta
self.rho = rho
self.q0 = q0
self.num_ants = num_ants
self.num_iterations = num_iterations
self.pheromone = None
self.best_fitness = None
self.best_w = None
self.best_b = None
# 初始化信息素
def init_pheromone(self):
self.pheromone = np.ones(2 * self.x.shape[1] + 1)
# 运行蚁群算法
def run(self):
self.init_pheromone()
n = self.x.shape[0]
ants = []
for i in range(self.num_ants):
w = np.zeros(self.x.shape[1])
b = 0.0
ant = Ant(w, b, self.x, self.y, self.c, self.kernel)
ants.append(ant)
for i in range(self.num_iterations):
for ant in ants:
ant.step(self.rho, self.q0, self.pheromone, self.alpha, self.beta)
best_ant = min(ants, key=lambda ant:ant.fitness)
if self.best_fitness is None or best_ant.fitness < self.best_fitness:
self.best_fitness = best_ant.fitness
self.best_w = best_ant.w
self.best_b = best_ant.b
for j in range(2 * self.x.shape[1] + 1):
self.pheromone[j] = (1 - self.rho) * self.pheromone[j] + sum([ant.alpha[j] for ant in ants])
return self.best_w, self.best_b
```
然后,我们可以使用以下代码进行测试:
```python
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 生成二分类数据集
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, random_state=2021)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2021)
# 创建蚁群优化算法支持向量机模型
ac = AntColony(X_train, y_train, 1.0, kernel='linear', num_ants=20, num_iterations=100)
w, b = ac.run()
# 在测试集上评估模型性能
y_pred = np.sign(np.dot(X_test, w) + b)
print('Accuracy:', accuracy_score(y_test, y_pred))
```
注意,上述代码中使用了sklearn库中的SVC类来计算适应度函数值,因此需要先安装sklearn库。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)