def draw_graph(datasets, n_clusters=2): plot_num = 1 alg_names = [] eps_list = [] for eps in np.arange(0.1, 1, step=0.1): alg_names.append('DBScan eps=%.1f' % eps) eps_list.append(eps) a = np.arange(len(alg_names)*2)+1 indices = np.hstack(a.reshape(len(alg_names), 2).T) elice_utils.draw_init() for dataset in datasets: y = dataset['0'] X = np.array(dataset[['1', '2']]) for alg_name, eps in zip(alg_names, eps_list): dbscan_result = run_DBScan(X, eps) elice_utils.draw_graph(X, dbscan_result, alg_name, plot_num, len(alg_names), indices) plot_num += 1 print(elice_utils.show_graph()) return dbscan_result
def draw_graph(datasets, n_clusters=2): # DB-scan 알고리즘에서 eps값이 변화함에 따라 같은 클러스터로 인식하는 범위가 달라지는 여부를 비교해볼 수 있다. # 예상대로 eps값이 커질수록 멀리 떨어진 부분을 같은 클러스터로 인식한다. plot_num = 1 alg_names = [] eps_list = [] for eps in np.arange(0.1, 1, step=0.1): alg_names.append("DBScan eps=%.1f" % eps) eps_list.append(eps) a = np.arange(len(alg_names) * 2) + 1 indices = np.hstack(a.reshape(len(alg_names), 2).T) elice_utils.draw_init() for dataset in datasets: temp = [] for idx in dataset.index: # print(str(dataset['1'][idx]) +', '+str(dataset['2'][idx])) temp.append([dataset["1"][idx], dataset["2"][idx]]) X = np.array(temp) for alg_name, eps in zip(alg_names, eps_list): dbscan_result = run_DBScan(X, eps) elice_utils.draw_graph(X, dbscan_result, alg_name, plot_num, len(alg_names), indices) plot_num += 1 print(elice_utils.show_graph()) return dbscan_result
def draw_graph(datasets, n_clusters=2): plot_num = 1 alg_names = [] eps_list = [] for eps in np.arange(0.1, 1, step=0.1): alg_names.append('DBScan eps=%.1f' % eps) eps_list.append(eps) a = np.arange(len(alg_names) * 2) + 1 indices = np.hstack(a.reshape(len(alg_names), 2).T) elice_utils.draw_init() for dataset in datasets: y = dataset['0'] X = np.array(dataset[['1', '2']]) for alg_name, eps in zip(alg_names, eps_list): dbscan_result = run_DBScan(X, eps) elice_utils.draw_graph(X, dbscan_result, alg_name, plot_num, len(alg_names), indices) plot_num += 1 print(elice_utils.show_graph()) return dbscan_result
def main(): C = 1.0 X, y = load_data() svc_linear = run_linear_SVM(X, y, C) svc_poly2 = run_poly_SVM(X, y, 2, C) svc_poly3 = run_poly_SVM(X, y, 3, C) svc_rbf = run_rbf_SVM(X, y, C) elice_utils.draw_graph(X, y, svc_linear, svc_poly2, svc_poly3, svc_rbf)
def main(): C = 1.0 X, y = load_data() pca, X_pca = run_PCA(X, 2) lda, X_lda = run_LDA(X, y, 2) svc_linear_pca = run_linear_SVM(X_pca, y, C) svc_rbf_pca = run_rbf_SVM(X_pca, y, C) svc_linear_lda = run_linear_SVM(X_lda, y, C) svc_rbf_lda = run_rbf_SVM(X_lda, y, C) elice_utils.draw_graph(X_pca, X_lda, y, svc_linear_pca, svc_rbf_pca, svc_linear_lda, svc_rbf_lda) print(elice_utils.show_graph())
def draw_graph(datasets, n_clusters=2, alg_name = 'KMeans'): plot_num = 1 elice_utils.draw_init() for dataset in datasets: # 3 kmeans_result = run_kmeans(X, n_clusters) elice_utils.draw_graph(X, kmeans_result, alg_name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return kmeans_result
def draw_graph(datasets, n_clusters=2, alg_name='KMeans'): plot_num = 1 elice_utils.draw_init() for dataset in datasets: y = dataset['0'] X = np.array(dataset[['1', '2']]) kmeans_result = run_kmeans(X, n_clusters) elice_utils.draw_graph(X, kmeans_result, alg_name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return kmeans_result
def draw_graph(datasets, n_clusters=2, alg_name = 'KMeans'): plot_num = 1 elice_utils.draw_init() for dataset in datasets: # 3 y = dataset.ix[:,0] X = np.vstack((dataset.ix[:,1],dataset.ix[:,2])).T kmeans_result = run_kmeans(X, n_clusters) elice_utils.draw_graph(X, kmeans_result, alg_name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return kmeans_result
def draw_graph(datasets, n_clusters=2, eps=0.2, alg_name = ['KMeans', 'DBScan']): plot_num = 1 elice_utils.draw_init() for dataset in datasets: # 4 X = np.vstack((dataset.ix[:,1],dataset.ix[:,2])).T kmeans_result = run_kmeans(X, n_clusters) dbscan_result = run_DBScan(X, eps) for name, algorithm in zip(alg_name, [kmeans_result, dbscan_result]): elice_utils.draw_graph(X, algorithm, name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return dbscan_result
def draw_graph(datasets, n_clusters=2, eps=0.2, alg_name=['KMeans', 'DBScan']): plot_num = 1 elice_utils.draw_init() for dataset in datasets: # 4 X = np.vstack((dataset.ix[:, 1], dataset.ix[:, 2])).T kmeans_result = run_kmeans(X, n_clusters) dbscan_result = run_DBScan(X, eps) for name, algorithm in zip(alg_name, [kmeans_result, dbscan_result]): elice_utils.draw_graph(X, algorithm, name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return dbscan_result
def draw_graph(datasets, n_clusters=2, alg_name = 'KMeans'): plot_num = 1 elice_utils.draw_init() for dataset in datasets: # 3 tmp_y = dataset.pop('0') y = np.array(tmp_y) tmp_X = dataset X = np.array(tmp_X) kmeans_result = run_kmeans(X, n_clusters) elice_utils.draw_graph(X, kmeans_result, alg_name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return kmeans_result
def draw_graph(datasets, n_clusters=2, alg_name='KMeans'): plot_num = 1 elice_utils.draw_init() for dataset in datasets: # 3 tmp_y = dataset.pop('0') y = np.array(tmp_y) tmp_X = dataset X = np.array(tmp_X) kmeans_result = run_kmeans(X, n_clusters) elice_utils.draw_graph(X, kmeans_result, alg_name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return kmeans_result
def draw_graph(datasets, n_clusters=2, eps=0.2, alg_name = ['KMeans', 'DBScan']): plot_num = 1 elice_utils.draw_init() for dataset in datasets: y = dataset['0'] X = np.array(dataset[['1', '2']]) kmeans_result = run_kmeans(X, n_clusters) dbscan_result = run_DBScan(X, eps) for name, algorithm in zip(alg_name, [kmeans_result, dbscan_result]): elice_utils.draw_graph(X, algorithm, name, plot_num) plot_num += 1 print(elice_utils.show_graph()) return dbscan_result