小程序实现过程分为三大步：1.数据采集及处理 2.模型训练 3.模型部署
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- 六轴加速度传感器
- 采样频率：20HZ
- 每次采集10s
- 保存为JSON格式
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- 每轴提取7个时域特征：最小值，最大值，最大值与最小值之差，均值，方差，标准差，正值个数
- 每个窗口共提取出6×7=42个特征
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利用朴素贝叶斯算法，仅提取时域特征，正确率大约为99.9%
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from csv import reader from csv import writer from random import seed from random import randrange from math import sqrt from math import exp from math import pi # load csvfile def load_csv(filename): dataset = [] with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # convert string column to float def str_col_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # convert string column to integer def str_col_to_int(dataset, column): for row in dataset: row[column] = int(row[column].strip()) # split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = [] dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for _ in range(n_folds): fold = [] while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = [] for fold in folds: train_set = list(folds) # 复制folds train_set.remove(fold) train_set = sum(train_set, []) # ☆单列表生成 test_set = [] for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # split the dataset by class values, returns a dictionary def separate_by_class(dataset): separated = dict() for i in range(len(dataset)): vector = dataset[i] class_value = vector[-1] if class_value not in separated: separated[class_value] = [] separated[class_value].append(vector) return separated # calculate the mean of a list of numbers def mean(numbers): return sum(numbers) / float(len(numbers)) # calculate the standard deviation of a list of numbers def stdev(numbers): avg = mean(numbers) variance = sum([(x - avg) ** 2 for x in numbers]) / float(len(numbers) - 1) return sqrt(variance) # calculate the mean, stdev and count for each column in a dataset def summarize_dataset(dataset): summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)] # zip(*)转置相关☆ del (summaries[-1]) return summaries # split dataset by class then calculate statistics for each row def summarize_by_class(dataset): separated = separate_by_class(dataset) summaries = dict() for class_value, rows in separated.items(): summaries[class_value] = summarize_dataset(rows) return summaries # calculate the Gaussian probability distribution function for x def calculate_probability(x, mean, stdev): if stdev != 0: exponent = exp(-((x - mean) ** 2 / (2 * stdev ** 2))) return (1 / (sqrt(2 * pi) * stdev)) * exponent elif x == mean: return 1 else: return 0 # calculate the probabilities of predicting each class for a given row def calculate_class_probabilities(summaries, row): total_rows = sum([summaries[label][0][2] for label in summaries]) # label 遍历 key # for label in summaries: # print(label,' ',summaries[label][0][2]) probabilities = dict() for class_value, class_summaries in summaries.items(): probabilities[class_value] = summaries[class_value][0][2] / float(total_rows) for i in range(len(class_summaries)): mean, stdev, _= class_summaries[i] probabilities[class_value] *= calculate_probability(row[i], mean, stdev) # 逐列 return probabilities # predict the class for a given row def predict(summaries, row): probabilities = calculate_class_probabilities(summaries, row) best_label, best_prob = None, -1 for class_value, probability in probabilities.items(): if best_label is None or probability > best_prob: best_prob = probability best_label = class_value return best_label def save_summaries(summar): with open('naiveRes.csv','w') as naive: w = writer(naive) for k,v in summar.items(): summar[k] = list(map(list,zip(*summar[k]))) summar[k].pop() summar[k][0].append(k*10) summar[k][1].append(k*10+1) w.writerow(summar[k][0]) w.writerow(summar[k][1]) # Naive Bayes Algorithm def naive_bayes(train, test): summarize = summarize_by_class(train) predictions = list() for row in test: output = predict(summarize, row) predictions.append(output) save_summaries(summarize) return predictions seed(30) filename = './features.csv' dataset = load_csv(filename) for i in range(len(dataset[0]) - 1): str_col_to_float(dataset, i) str_col_to_int(dataset, len(dataset[0]) - 1) n_folds = 5 scores = evaluate_algorithm(dataset, naive_bayes, n_folds) # 以函数对象作为参数 print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores) / float(len(scores))))
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JavaScript重写模型，离线识别
mean: [ [动作0各特征的均值],[动作1各特征的均值],[动作2各特征的均值],[动作3各特征的均值] ],
stdev:[ [动作0各特征的标准差],[动作1各特征的标准差],[动作2各特征的标准差],[动作3各特征的标准差] ], /*根据特征值计算四个动作的概率*/
naive_calc_prob:function(fea){
var mean = this.data.mean;
var stdev = this.data.stdev;
var rows = this.data.rows;
var sum_row = 0;
var prob = new Array(4);
for(let i=0;i<4;i++){
sum_row += rows[i];
}
for(let i=0;i<4;i++){
prob[i] = rows[i]/sum_row;
for(let j=0;j<fea.length;j++){
if(stdev[i][j]!=0){
prob[i] *= Math.exp(-((fea[j] - mean[i][j])*(fea[j] - mean[i][j]) / (
2 * stdev[i][j] * stdev[i][j])));
prob[i] *= (1/(Math.sqrt(2*Math.PI) * stdev[i][j]));
}
else if(fea[j]==mean[i][j])
prob[i] *=1;
else
prob[i] *=0;
}
}
return prob;
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- 点击按钮后，按钮变为disabled状态，以防止在测试过程中的误触
- 30s计时过程中以3s为间隔播报当前动作
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