Blog

Which factor(s) increase the likelihood of poor body mechanics?

Which statement regarding the biological effects of diagnostic ultrasound is TRUE?

Which situation is considered an abnormal finding during a first-trimester pregnancy?

The pancreas and surrounding vascular landmarks should be examined from the level of what?

The dose to air is found to be 3.3 Gy. What is the exposure?

What is radiographic film useful for? 

When is kerma at its maximum?

Consider the one-dimensional (i.e., = 1) training dataset in the figure below (left side, red dots represent data pairs ).   Building on the previous tree, find the regression tree with three leaves having the smallest training MSE. What is the threshold value

In this problem, we have sketched up the code for the K-Means Clustering algorithm. Please choose options to fill in the blanks. import numpy as np import matplotlib.pyplot as plt def kmeans(X,K,iteration):     N = len(X) # Number of data points     labels = np.zeros((N,1)) # Cluster labels for each data point     centroids = np.zeros((K,X.shape[1])) # Centroid of each cluster     # Innitialize: Randomly assign a number C(i) in (1,…,K) to each index i = 1…N     for i in range(len(labels)):         labels[i] = np.random.randint(0,K)             for iteration in range(iteration):          # Compute the centroid of cluster K         for k in range(K):             dp = X[np.where(labels == k)[0]]             centroids[k] = _________(1)___________                     # Assign observation n to the cluster with closest centroid         for n in range(N):             distance = np.linalg.norm(X[n]-centroids,axis=1)             labels[n] = _________(2)___________                 # Compute the distance between each data point and their centroids     within_cluster_distance = 0     for m in range(N):         within_cluster_distance += _________(3)___________             return within_cluster_distance     k_list = [] for i in range(1,10):     k_list.append(kmeans(X1,i,10))     x = np.arange(1,10) plt.plot(x,k_list) plt.xlabel(‘K’) plt.ylabel(‘Within Cluster Distance’) plt.show() The format of input $$X$$ is shown below: What should go in the second blank(2)?

Consider a dataset with points and two classes (red and blue) indicated in the figure below. (Note that (0, 0) and (1, 1) are blue points whereas (1, 0) and (0, 1) are red points).   Which one of the following equations represents a separating hyperplane for the lifted three-dimensional dataset?