int n_samples, n_features; n_samples = m_ldDatalen; if(m_bLastColumnisClassfication) { n_features = m_dFieldNum - 1; } else { n_features = m_dFieldNum; } n_features --; //特征数减1,最后一个为回归Y值 std::vector<std::vector<double>> X(n_samples, std::vector<double>(n_features)); std::vector<double> y(n_samples); for (int i = 0; i < n_samples; i++) { for (int j = 0; j < n_features; j++) { X[i][j] = g_fData[j*n_samples+i]; } y[i] = g_fData[n_features*n_samples+i]; } // 训练模型 //SGDRegression model(0.01, 1000, 1e-4); //model.fit(X, y); // 训练模型 ProbitRegression model(n_features); double learning_rate = 0.01; int num_iterations = 10000; model.fit(X, y, learning_rate, num_iterations);
时间: 2024-03-29 11:40:52 浏览: 24
这段代码看起来像是在对数据进行预处理和训练机器学习模型,可以看出数据的特征数和样本数分别由变量n_features和n_samples表示。然后通过双重循环,将原始数据中的特征值和回归Y值分别存入二维向量X和一维向量y中。
接下来,代码创建一个ProbitRegression类的对象model,并用fit()函数对数据进行训练。这里采用的是梯度下降的方式进行训练,其中学习率和训练次数在fit()函数中分别设置为learning_rate和num_iterations。
相关问题
基于GR,LLD,LLS,AC四个测井数据,进行pso优化xgboost超参数max_depth/n_estimators/max_features/min_samples_split,预测TOC的代码
以下是一个基于GR、LLD、LLS、AC四个测井数据进行 TOC 预测的 Python 代码,使用了 PSO 算法优化 XGBoost 的超参数 max_depth、n_estimators、max_features 和 min_samples_split:
```python
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from pyswarm import pso
# 加载数据集
data = pd.read_csv('data.csv')
# 分割数据集
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=42)
# 定义评估函数
def evaluate_model(params):
max_depth = int(params[0])
n_estimators = int(params[1])
max_features = int(params[2])
min_samples_split = int(params[3])
model = xgb.XGBRegressor(max_depth=max_depth, n_estimators=n_estimators, max_features=max_features, min_samples_split=min_samples_split)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
return mse
# 定义优化函数
def optimize_model(params):
mse = evaluate_model(params)
return mse
# 定义超参数空间
bounds = [(1, 10), (10, 100), (1, 10), (2, 10)]
# 运行 PSO 算法
params = pso(optimize_model, bounds, swarmsize=10, maxiter=50)
# 训练模型
max_depth = int(params[0])
n_estimators = int(params[1])
max_features = int(params[2])
min_samples_split = int(params[3])
model = xgb.XGBRegressor(max_depth=max_depth, n_estimators=n_estimators, max_features=max_features, min_samples_split=min_samples_split)
model.fit(X_train, y_train)
# 预测结果
y_pred = model.predict(X_test)
# 计算 MSE
mse = mean_squared_error(y_test, y_pred)
# 打印结果
print('Best parameters:', params)
print('MSE:', mse)
```
在这个示例中,我们首先加载了数据集,然后将其分割成训练集和测试集。接下来,我们定义了一个评估函数 evaluate_model(),用于评估 XGBoost 模型的性能。在这个示例中,我们使用了 XGBRegressor 类来定义模型,并设置了四个超参数:max_depth、n_estimators、max_features 和 min_samples_split。然后,我们定义了一个优化函数 optimize_model(),用于优化 XGBoost 的超参数。在这个示例中,我们使用了 PSO 算法来搜索超参数空间。我们定义了超参数空间 bounds,它包含了四个超参数的取值范围。最后,我们使用 XGBRegressor 类训练 XGBoost 模型,并使用 predict() 函数进行预测。我们计算了预测结果的 MSE,并打印出最优超参数和 MSE。
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
这段程序是一个分类模型的辅助函数,包括了绘制决策边界、sigmoid函数和加载数据集的函数。具体实现如下:
```python
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):
# 设置最小值和最大值,并给它们一些填充
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
# 生成一个网格,网格中点的距离为h
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# 对整个网格预测函数值
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# 绘制轮廓和训练样本
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 # 样本数量
N = int(m / 2) # 每个类的样本数量
# 生成数据集
D = 2 # 特征维度
X = np.zeros((m, D)) # 特征矩阵
Y = np.zeros((m, 1), dtype='uint8') # 标签向量
a = 4 # 花的最大半径
for j in range(2):
ix = range(N*j, N*(j+1))
t = np.linspace(j*3.12, (j+1)*3.12, N) + np.random.randn(N)*0.2 # theta
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
X[ix] = np.c_[r*np.sin(t), r*np.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
```
这段程序中包含了以下函数:
- `plot_decision_boundary(model, X, y)`:绘制分类模型的决策边界,其中`model`是分类模型,`X`是特征矩阵,`y`是标签向量。
- `sigmoid(x)`:实现sigmoid函数。
- `load_planar_dataset()`:加载一个二维的花瓣数据集。
- `load_extra_datasets()`:加载五个其他数据集。