步骤4: 使用拟合函数进行参数估计 在这一阶段,我们将使用curve_fit函数估计参数。 # 拟合数据initial_guess=(1,5,5,1,1)# 初始参数猜测params,covariance=curve_fit(gaussian_2d,(x.ravel(),y.ravel()),data.ravel(),p0=initial_guess)# 提取拟合的参数amplitude,x0,y0,sigma_x,sigma_y=paramsprint(f'...
# 使用拟合结果生成高斯函数Z_fit=gaussian_2d(X,Y,*popt).reshape(Z_noisy.shape)# 绘制原始数据和拟合曲面fig=plt.figure()ax=fig.add_subplot(111,projection='3d')ax.scatter(X_flat,Y_flat,Z_noisy_flat,color='b',label='Noisy data',s=1)ax.plot_surface(X,Y,Z_fit,color='r',alpha=0.5,...
gaussian_maximum_likelihood( self._labelled_x, n_category, dim) for dim in range(len(self._x))] self._data = data def func(input_x, tar_category): # 将输入转换成二维数组(矩阵) input_x = np.atleast_2d(input_x).T rs = np.ones(input_x.shape[1]) for d, xx in enumerate(...
# Reshape the selected good features to 2D (if needed) good_features_reshaped = good_features.reshape(good_features.shape[0], -1) # Perform t-SNE analysis tsne = TSNE(n_components=2, random_state=42) tsne_results = tsne.fit_transform(good_features_reshaped) # Perform Gaussian Mixture ...
def match_corner(coordinates, window_ext=3): row, col = np.round(coordinates).astype(np.intp) window_original = image_original[row-window_ext:row+window_ext+1, col-window_ext:col+window_ext+1, :] weights = gaussian_weights(window_ext, 3) weights = np.dstack((weights, weights, weight...
在GaussianProcessRegressor中,我们使用fit方法来进行训练。 # 训练模型 gp.fit(X_train, y_train) 5. 进行预测 训练完成后,我们可以使用predict方法进行预测。这个方法返回预测值以及预测值的标准差。 ```python 定义测试数据 X_test = np.atleast_2d(np.linspace(0, 10, 100)).T 进行预测 y_pred, sigma...
...首先,正态分布是最重要的一种概率分布,正态分布(Normal distribution),也称高斯分布(Gaussian distribution),具体详细的介绍可自行网上查阅资料; 其次,如下图中所示的...:分位数、中位数、众数等; 再者,就是今天要重点介绍的箱型图,如下图所示 待会要分享的 Python 程序就是对箱型图中上下边缘值的计算实现...
[:,:,::-1]),plt.title('Averaging')plt.xticks([]), plt.yticks([])plt.subplot(223),plt.imshow(blur_1[:,:,::-1]),plt.title('Gaussian')plt.xticks([]), plt.yticks([])plt.subplot(224),plt.imshow(blur_1[:,:,::-1]),plt.title('B...
3.3,高斯核函数(Gaussian Kernel) 高斯核函数,在SVM中也称为 径向基核函数(Radial Basisi Function,RBF),它是libsvm默认的核函数,当然也是sklearn默认的核函数,表达式为: 其中r 大于0,需要自己调参定义,不过一般情况,我们都使用高斯核函数。 3.4,Sigmoid核函数(Sigmoid Kernel) ...
gmm = GaussianMixture(n_components=3) gmm.fit(multimodal_dist.reshape(-1, 1)) means = gmm.means_ # Conver covariance into Standard Deviation standard_deviations = gmm.covariances_**0.5 # Useful when plotting the distributions later weights = gmm.weights_ ...