knn

问题 I have a matriz ZZ. After I ran prcomp and chose the first 5 PCs I get data_new : P= prcomp(zz) data_new = P$x[,1:5] then I split into training set and test set pca_train = data_new[1:121,] pca_test = data_new[122:151,] and use KNN: k <- knn(pca_train, pca_test, tempGenre_train[,1], k = 5) a <- data.frame(k) res <- length(which(a!=tempGenre_test)) Each time I run these 3 last rows, I get a different value in res . Why? Is there a better way to check what is the test error? 回答1: From the

问题 How can only the boundary values be extracted , or returned, from .predict , for sklearn.neighbors.KNeighborsClassifier()? MRE import pandas as pd import numpy as np from sklearn.datasets import load_iris from sklearn.neighbors import KNeighborsClassifier import seaborn as sns import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap # prepare data iris = load_iris() X = iris.data y = iris.target df = pd.DataFrame(X, columns=iris.feature_names) df['label'] = y species_map =

问题 Hi I am using the caret package and training a model with a knn algorithm but I am running into an error. I am using the german credit data and this is the structure of the data frame 'data.frame': 1000 obs. of 21 variables: $ checking_balance : Factor w/ 4 levels "< 0 DM","> 200 DM",..: 1 3 4 1 1 $ months_loan_duration: int 6 48 12 42 24 36 24 36 12 30 ... $ credit_history : Factor w/ 5 levels "critical","delayed",..: 1 5 1 5 $ purpose : Factor w/ 10 levels "business","car (new)",..: 8 8 5 $

问题 Hi I am using the caret package and training a model with a knn algorithm but I am running into an error. I am using the german credit data and this is the structure of the data frame 'data.frame': 1000 obs. of 21 variables: $ checking_balance : Factor w/ 4 levels "< 0 DM","> 200 DM",..: 1 3 4 1 1 $ months_loan_duration: int 6 48 12 42 24 36 24 36 12 30 ... $ credit_history : Factor w/ 5 levels "critical","delayed",..: 1 5 1 5 $ purpose : Factor w/ 10 levels "business","car (new)",..: 8 8 5 $

问题 I have trained a KNN on top of MobileNet logits results using TensorFlowJS. And I want to know how can I export the result of the MobileNet + KNN to a TFLite model. const knn = knnClassifier.create() const net = await mobilenet.load() const handleTrain = (imgEl, label) => { const image = tf.browser.fromPixels(imgEl); const activation = net.infer(image, true); knn.addExample(activation, label) } 回答1: 1. Save the model Save the model this example saves the file to the native file system or if

问题 Where can I find an serial C/C++ implementation of the k-nearest neighbour algorithm? Do you know of any library that has this? I have found openCV but the implementation is already parallel. I want to start from a serial implementation and parallelize it with pthreads openMP and MPI. Thanks, Alex 回答1: How about ANN? http://www.cs.umd.edu/~mount/ANN/. I have once used the kdtree implementation, but there are other options. Quoting from the website: "ANN is a library written in C++, which

问题 I have a CSV file (corpus.csv) with graded abstracts (text) in the following format in corpus: Institute, Score, Abstract ---------------------------------------------------------------------- UoM, 3.0, Hello, this is abstract one UoM, 3.2, Hello, this is abstract two and yet counting. UoE, 3.1, Hello, yet another abstract but this is a unique one. UoE, 2.2, Hello, please no more abstract. I am trying to create a KNN classification program in python, which is able to get an user input

问题 I have a CSV file (corpus.csv) with graded abstracts (text) in the following format in corpus: Institute, Score, Abstract ---------------------------------------------------------------------- UoM, 3.0, Hello, this is abstract one UoM, 3.2, Hello, this is abstract two and yet counting. UoE, 3.1, Hello, yet another abstract but this is a unique one. UoE, 2.2, Hello, please no more abstract. I am trying to create a KNN classification program in python, which is able to get an user input

问题 I'm using the Kobe Bryant Dataset. I wish to predict the shot_made_flag with KnnRegressor. I've used game_date to extract year and month features: # covert season to years kobe_data_encoded['season'] = kobe_data_encoded['season'].apply(lambda x: int(re.compile('(\d+)-').findall(x)[0])) # add year and month using game_date kobe_data_encoded['year'] = kobe_data_encoded['game_date'].apply(lambda x: int(re.compile('(\d{4})').findall(x)[0])) kobe_data_encoded['month'] = kobe_data_encoded['game