scikit-learn

Modifying old GaussianProcessor example to run with GaussianProcessRegressor

由 时光总嘲笑我的痴心妄想 提交于 2021-01-07 08:57:08
问题 I have an example from a data science book I am trying to run in a Jupyter notebook. The code sippet looks like this from sklearn.gaussian_process import GaussianProcess # define the model and draw some data model = lambda x: x * np.sin(x) xdata = np.array([1, 3, 5, 6, 8]) ydata = model(xdata) # Compute the Gaussian process fit gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1, random_start=100) gp.fit(xdata[:, np.newaxis], ydata) xfit = np.linspace(0, 10, 1000) yfit,

Modifying old GaussianProcessor example to run with GaussianProcessRegressor

由 别说谁变了你拦得住时间么 提交于 2021-01-07 08:56:07
问题 I have an example from a data science book I am trying to run in a Jupyter notebook. The code sippet looks like this from sklearn.gaussian_process import GaussianProcess # define the model and draw some data model = lambda x: x * np.sin(x) xdata = np.array([1, 3, 5, 6, 8]) ydata = model(xdata) # Compute the Gaussian process fit gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1, random_start=100) gp.fit(xdata[:, np.newaxis], ydata) xfit = np.linspace(0, 10, 1000) yfit,

Modifying old GaussianProcessor example to run with GaussianProcessRegressor

由 我与影子孤独终老i 提交于 2021-01-07 08:54:33
问题 I have an example from a data science book I am trying to run in a Jupyter notebook. The code sippet looks like this from sklearn.gaussian_process import GaussianProcess # define the model and draw some data model = lambda x: x * np.sin(x) xdata = np.array([1, 3, 5, 6, 8]) ydata = model(xdata) # Compute the Gaussian process fit gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1, random_start=100) gp.fit(xdata[:, np.newaxis], ydata) xfit = np.linspace(0, 10, 1000) yfit,

SciKit-Learn 可视化数据:主成分分析(PCA)

由 自古美人都是妖i 提交于 2021-01-07 08:35:37
保留版权所有，转帖注明出处 <div class="article-child "><h2>章节</h2><ul><li class="page_item page-item-4067"><a href="https://www.qikegu.com/docs/4067">SciKit-Learn 加载数据集</a></li> <li class="page_item page-item-4071"><a href="https://www.qikegu.com/docs/4071">SciKit-Learn 数据集基本信息</a></li> <li class="page_item page-item-4075"><a href="https://www.qikegu.com/docs/4075">SciKit-Learn 使用matplotlib可视化数据</a></li> <li class="page_item page-item-4080"><a href="https://www.qikegu.com/docs/4080">SciKit-Learn 可视化数据:主成分分析(PCA)</a></li> <li class="page_item page-item-4082"><a href="https://www.qikegu.com/docs/4082">SciKit

Proper way to subclass from sklearn with * argument

由 梦想与她 提交于 2021-01-06 02:52:30
问题 I am trying to subclass from sklearn.svm.LinearSVC and noticed the * argument in the signature. I'm not sure if this * refers to **kwargs or *args or something else. I am trying subclass the init function as follows. In this scenario I'm have added a single additional argument new_string_in_subclass the init function. from sklearn.svm import LinearSVC class LinearSVCSub(LinearSVC): def __init__(self, penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit

Proper way to subclass from sklearn with * argument

由 北慕城南 提交于 2021-01-06 02:49:48
问题 I am trying to subclass from sklearn.svm.LinearSVC and noticed the * argument in the signature. I'm not sure if this * refers to **kwargs or *args or something else. I am trying subclass the init function as follows. In this scenario I'm have added a single additional argument new_string_in_subclass the init function. from sklearn.svm import LinearSVC class LinearSVCSub(LinearSVC): def __init__(self, penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit

t-SNE generates different results on different machines

由 馋奶兔 提交于 2021-01-05 11:56:27
问题 I have around 3000 datapoints in 100D that I project to 2D with t-SNE. Each datapoint belongs to one of three classes. However, when I run the script on two separate computers I keep getting inconsistent results. Some inconsistency is expected as I use a random seed, however one of the computers keeps getting better results (I use a macbook pro and a stationary machine on Ubuntu). I use the t-SNE implementation from Scikit-learn. The script and data is identical, I've manually copied the

Define k-1 cluster centroids — SKlearn KMeans

由 别说谁变了你拦得住时间么 提交于 2021-01-05 08:57:39
问题 I am performing a binary classification of a partially labeled dataset. I have a reliable estimate of its 1's, but not of its 0's. From sklearn KMeans documentation: init : {‘k-means++’, ‘random’ or an ndarray} Method for initialization, defaults to ‘k-means++’: If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. I would like to pass an ndarray, but I only have 1 reliable centroid, not 2. Is there a way to maximize the entropy between the K

Define k-1 cluster centroids — SKlearn KMeans

由 烈酒焚心 提交于 2021-01-05 08:56:43
问题 I am performing a binary classification of a partially labeled dataset. I have a reliable estimate of its 1's, but not of its 0's. From sklearn KMeans documentation: init : {‘k-means++’, ‘random’ or an ndarray} Method for initialization, defaults to ‘k-means++’: If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. I would like to pass an ndarray, but I only have 1 reliable centroid, not 2. Is there a way to maximize the entropy between the K

Plotting the KMeans Cluster Centers for every iteration in Python

由 送分小仙女□ 提交于 2021-01-05 07:22:45
问题 I created a dataset with 6 clusters and visualize it with the code below, and find the cluster center points for every iteration, now i want to visualize demonstration of update of the cluster centroids in KMeans algorithm. This demonstration should include first four iterations by generating 2×2-axis figure. I found the points but i cant plot them, can you please check out my code and by looking that, help me write the algorithm to scatter plot? Here is my code so far: import seaborn as sns
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