for k_iter in range(K): temp += Pik[0,k_iter] * NDimensionGaussian(Xn[:,n_iter],Uk[:,k_iter],Cov[k_iter]) energy += numpy.log(temp) return float(energy) def SequentialEMforMixGaussian(InputData,K): #初始化piK pi_Cof=numpy.mat(numpy.ones((1,K))*(1.0/float(K))) X=numpy...
col-window_ext:col+window_ext+1, :] weights = gaussian_weights(window_ext, 3) weights = np.dstack((weights, weights, weights)) SSDs = [] for coord_row, coord_col in coordinates_warped: window_warped = image
'''归一化数据并作图''' scaler = StandardScaler() scaler.fit(X) x_train = scaler.transform(X) 使用PCA模型拟合数据,并降维 n_components对应要将的维度 '''拟合数据''' K=1 # 要降的维度 model = pca.PCA(n_components=K).fit(x_train) # 拟合数据,n_components定义要降的维度 Z = model.tra...
應用高斯濾波器的拉普拉斯算子,由高斯差分濾波器近似: >>>filtered_image =difference_of_gaussians(astronaut(),2,...channel_axis=-1) 對灰度圖像應用高斯差分濾波器,每個軸使用不同的 sigma 值: >>>fromskimage.dataimportcamera>>>filtered_image =difference_of_gaussians(camera(), (2,5), (3,20)) ...
gplearnWelcome to gplearn’s documentation:python GP库,提供了符号回归,分类等方法 1.gplearn 1.1 Introduction(介绍) gplearn用python实现Genetic Programming,和scikit-learn一样提供了可兼容API,GP在很多领域得到了广泛应用,gplearn主要用于解决Symbolic regression(符合回归)问题。Symbolic regression是一种机器学习技术,...
X_scaled = scaler.fit_transform(X) #checking results print(pd.DataFrame(X_scaled).describe()) X_scaled.shape 0 1 2 3 4 \ count 25018.000000 25018.000000 25018.000000 25018.000000 25018.000000 mean -0.228609 0.228609 -0.437365 -0.228609 0.011229 ...
D’Agostino’s K^2 Test 这个D‘Agostino K^2检验根据数据计算汇总统计数据,即峰度和偏度,以确定数据分布是否偏离了以拉尔夫·达戈斯蒂诺命名的正态分布。 偏斜是对一个分布向左或向右推多大的量化,这是对分布中不对称的一种度量。 峰度量化分布在尾部的数量。这是一种简单而常用的正态性统计检验。 D‘Agostin...
scaler = scaler.fit(training_df[col_name].values.reshape(-1, 1)) train_values_standardized = scaler.transform(training_df[col_name].values.reshape(-1, 1)) training_df[col_name] = train_values_standardized test_values_standardized = scaler.transform(testing_df[col_name].values.reshape(-1,...
fit 1D (multiple) data including: spectra, surface brightness profiles, light curves, general ASCII arrays fit 2D images/surfaces in Poisson/Gaussian regime build complex model expressions import and use your own models use appropriate statistics for modeling Poisson or Gaussian data ...
The funstools.Decompose.initial_fit() optimizes this initial Gaussian parameters (ai, mi, si) to accurately reproduce smoothed spectrum using scipy.optimize.curve_fit and multiple Gaussian function expressed as the sum of Nc Gaussian functions. Gaussian parameters for each velocity component of each...