直方图基础 直方图(Histogram)是将数据分成多个区间并统计每个区间内的数据数量的图形。通过观察直方图,我们可以对数据的分布情况有一个直观的了解。 高斯拟合 高斯拟合(Gaussian Fit)是通过高斯(Normal)分布函数来拟合数据。高斯分布在许多自然现象中广泛存在,可通过以下概率密度函数表示: [ f(x) = \frac{1}{\sigma...
plt.ylabel('频率')# 生成正态曲线的数据x1 = np.linspace(titanic.Age.min(), titanic.Age.max(),1000) normal = stats.norm.pdf(x1, titanic.Age.mean(), titanic.Age.std())# 绘制正态分布曲线line1, = plt.plot(x1,normal,'r-', linewidth =2)# 生成核密度曲线的数据kde = stats.gaussian_k...
最后,我们将直方图和高斯拟合曲线放在同一张图上进行展示,并输出拟合曲线的参数。 plt.hist(data,bins=10,edgecolor='black')plt.plot(x,y,'r-',label='Fit')plt.xlabel('Value')plt.ylabel('Frequency / Probability Density')plt.title('Histogram with Gaussian Fit')plt.legend()plt.show()print(f"拟...
gaussian_kde(data, bw_method='silverman') kde2 = stats.gaussian_kde(d2, bw_method='silverman') xs = np.linspace(-10, 10, num=200) #plt.plot(data) plt.plot(xs, kde1(xs)) plt.plot(xs, kde2(xs)) plt.plot(xs, n.pdf(xs), color='k') num_bins=100 h = np.histogram(data...
('Gaussian colored noise') # this is an inset axes over the main axes a = plt.axes([.65, .6, .2, .2], axisbg='y') n, bins, patches = plt.hist(s, 400, normed=1) plt.title('Probability') plt.xticks([]) plt.yticks([]) # this is another inset axes over the main axes...
gmm = GaussianMixture(n_components=3) labels = gmm.fit_predict(X) 可以检查EM算法收敛需要多少次迭代: print(gmm.n_iter_) 2 EM算法只需两次迭代即可收敛。检查估计的GMM参数: print('Weights:', gmm.weights_) print('Means:\n', gmm.means_) ...
>>> blurred_face = ndimage.gaussian_filter(noisy_face, sigma=3) >>> median_face = ndimage.median_filter(noisy_face, size=5) >>> from scipy import signal >>> wiener_face = signal.wiener(noisy_face, (5, 5)) 在其它过滤器scipy.ndimage.filters和scipy.signal可应用于图像。
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...
deffit_curve():globalPlot1 ds = Plot1.dsiflen(ds) ==0: log('Error: no curve to fit in Plot1.\n')returnfordinds:ifd.title =='fitting': Plot1.remove_dataset(d) d0 = ds[0] fitting =Fitting(GAUSSIAN_FITTING)try: fitting.set_histogram(d0, fit_min.value, fit_max.value) ...
# gaussian samples nm_large = norm(scale = 0.1, loc = 0.5) x_data_large = nm_large.rvs(size = N_data) x_data = np.concatenate((x_data, x_data_large)) # uniform samples uni = uniform x_data_uni = uni.rvs(size = int(N_data / 2)) ...