homework作业 | Machine Learning | report – Machine Learning

Machine Learning

homework作业 | report – 这是一个Machine Learning的practice, 考察k-mean算法的理解, 是比较有代表性的机器学习等代写方向, 这是值得参考的homework代写的题目

report代写 代做report

My sites/ 21S-STATS413-80/ Homeworks/  homework 05 --- Boosting & Clustering Spring^2021 - Week 8
Spring 2021 - STATS413-80 - WU

Machine Learning

Homework 05 – — Boosting & Clustering

Clustering Try K-mean and spectral clustering on toy dataset. Follow Tutorial: It use sklearn (install it by pip install scikit-learn) package to perform k-means.

Detailed requirement

1. As usual, write introduction, key equations, and kernel functions.
2. Read and play the tutorial. Use the following code to produce the toy data.
np.random.seed(1)

def xy(eta, n): theta = np.random.uniform(0, 2 * np.pi, n) (^) x = eta * np.cos(theta) + np.random.normal(0, 0.05, n) y = eta * np.sin(theta) + np.random.normal(0, 0.05, n) return x, y (^) x0, y0 = xy(0.1, 50) x1, y1 = xy(1.0, 200) x2, y2 = xy(2.0, 400) x = np.concatenate((x0, x1, x2), axis=0) (^) y = np.concatenate((y0, y1, y2), axis=0) X = np.transpose(np.vstack((x, y))) That should looks like this:

  1. Use gaussian kernel in computing similarity matrix. Change delta in the gaussian kernel to {0.1, 0.2, 0.5, 1}. See SpectralClustering — Notes section and Attributes: affinity_matrix_. Print the similarity matrix for each delta. (n*n grid, darker as higher value, brighter as lower value). You may use plt.matshow(). See example: Discuss how delta affect the similarity matrix.
  2. Train 5 clustering, k-means, spectral clustering with delta {0.1, 0.2, 0.5, 1}. For each of them, visualize the clustering results like the last two images in tutorial. Meanwhile, compare the accuracy for these 5 methods.

Grading (total 100 pts)

Homework  report (90pts) 
Get the mobile app
Clustering (90pts)
introduction: 5pts
main equation, structures, equations: 30pts
Similarity matrix graph: 20pts (5pts for each)
Result visualization: 25pts (5 for each)
Accuracy comparison: 10pts
Submit the code (py or ipynb): 10pts

Submission status

Submission status No attempt
Grading status Not graded
Due date Sunday, 30 May 2021, 11:59 PM PDT
Time remaining 10 days 5 hours
Last modified -
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