def main(): comp_range = [2, 5, 10, 20, 50, 100, 200, 500, 750, 1000, 1200, 1500, 2000] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True) rbf_scores, linear_scores = runSelectKBest(X_train, X_test, y_train, y_test, comp_range) draw(comp_range, rbf_scores, 'rbf') draw(comp_range, linear_scores, 'linear')
def main(): k_range = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=False, ifScale=True, suffix='_LDA') X_train_proj, X_test_proj = runMLKR(X_train, X_test, y_train, y_test) KNN.runKNN(X_train_proj, X_test_proj, y_train, y_test, k_range, metric='euclidean', metric_params=None, label='_MLKR_euclidean')
def main(): comp_range = [0.1, 0.2, 0.3, 0.5, 0.7, 0.9] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True) rbf_scores, linear_scores, dimension = runVarianceThreshold( X_train, X_test, y_train, y_test, comp_range) draw(comp_range, rbf_scores, dimension, 'rbf') draw(comp_range, linear_scores, dimension, 'linear')
def main(): k_range = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] LMNN_k_range = [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=False, ifScale=True, suffix='_LDA') for i in LMNN_k_range: X_train_proj, X_test_proj = runLMNN(X_train, X_test, y_train, y_test, i) KNN.runKNN(X_train_proj, X_test_proj, y_train, y_test, k_range, metric='euclidean', metric_params=None, label='_LMNN_euclidean_k='+str(i))
def coarseTuning(k=5): C_range = np.logspace(-5, 5, 11) X_train, X_test, y_train, y_test = loadDataDivided() rbfT = tuningThread(X_train, X_test, y_train, y_test, C_range, 'rbf', k, 'coarse') linearT = tuningThread(X_train, X_test, y_train, y_test, C_range, 'linear', k, 'coarse') rbfT.start() linearT.start() rbfT.join() linearT.join()
def fineTuning(k=5): C_range_rbf = [5.0, 10.0, 20.0, 50.0] C_range_linear = [0.0005, 0.001, 0.002, 0.005] X_train, X_test, y_train, y_test = loadDataDivided() rbfT = tuningThread(X_train, X_test, y_train, y_test, C_range_rbf, 'rbf', k, 'fine') linearT = tuningThread(X_train, X_test, y_train, y_test, C_range_linear, 'linear', k, 'fine') rbfT.start() linearT.start() rbfT.join() linearT.join()
def main(): comp_range_bh = [2, 3] ppl_range = [10.0, 20.0, 30.0, 40.0, 50.0] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True) for ppl in ppl_range: print("\nppl=%0.2d\n" % (ppl)) rbf_scores_bh, linear_scores_bh = runTSNE(X_train, X_test, y_train, y_test, comp_range_bh, ppl, 'barnes_hut') draw(comp_range_bh, rbf_scores_bh, 'rbf', ppl, 'barnes_hut') draw(comp_range_bh, linear_scores_bh, 'linear', ppl, 'barnes_hut')
def main(): rbf_scoresS = [] linear_scoresS = [] comp_range = [2, 3, 10, 20, 50, 100, 200, 500, 1000, 2000] neigh_range = [2, 4, 8, 16] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True) for n_neigh in neigh_range: print('n_neigh=%d' % (n_neigh)) rbf_scores, linear_scores = runIsomap(X_train, X_test, y_train, y_test, comp_range, n_neigh) rbf_scoresS.append(rbf_scores) linear_scoresS.append(linear_scores) draw(comp_range, neigh_range, rbf_scoresS, 'rbf') draw(comp_range, neigh_range, linear_scoresS, 'linear')
def main(): dim_range = [40] k_range = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True, ifScale=True, suffix='') for dim in dim_range: print("dim: %d, method: LDA, metric: %s" % (dim, "euclidean")) X_train_proj, X_test_proj = runLDA(X_train, X_test, y_train, y_test, dim) KNN.runKNN(X_train_proj, X_test_proj, y_train, y_test, k_range, metric='euclidean', metric_params=None, label=str(dim) + '_LDA_euclidean')
def main(): #kernel_range = ['linear', 'rbf', 'poly', 'sigmoid', 'cosine'] #dim_range = [50, 500, 2048] #k_range = [9] #metric_range = ['euclidean', 'manhattan', 'chebyshev'] #for dim in dim_range: # for metric in metric_range: # if dim != 2048: # for kernel in kernel_range: # print("dim: %d, kernel: %s, metric: %s" % (dim, kernel, metric)) # X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True, ifScale=False, suffix='_' + str(dim) + '_' + kernel) # KNN.runKNN(X_train, X_test, y_train, y_test, k_range, metric=metric, metric_params=None, label=str(dim) + '_' + kernel + '_' + metric + '_9') # else: # X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True, ifScale=False, suffix='') # print("dim: %d, metric: %s" % (dim, metric)) # KNN.runKNN(X_train, X_test, y_train, y_test, k_range, metric=metric, metric_params=None, label=str(dim) + '_' + metric + '_9') k_range = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] #for dim in dim_range: #if dim != 2048: # for kernel in kernel_range: # print("dim: %d, kernel: %s, metric: %s" % (dim, kernel, "cosine")) # X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True, ifScale=False, suffix='_' + str(dim) + '_' + kernel) # KNN.runKNN(X_train, X_test, y_train, y_test, k_range, metric=cosine, metric_params=None, label=str(dim) + '_' + kernel + '_cosine1') #else: X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=False, ifScale=False, suffix='') print("dim: %d, metric: %s" % (2048, "cosine")) KNN.runKNN(X_train, X_test, y_train, y_test, k_range, metric=cosine, metric_params=None, label=str(2048) + '_cosine1')
def main(): comp_range = [2, 3, 5, 10, 20, 30, 40] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True) rbf_scores, linear_scores = runLDA(X_train, X_test, y_train, y_test, comp_range) draw(comp_range, rbf_scores, 'rbf') draw(comp_range, linear_scores, 'linear')
def main(): comp_range = [2, 4, 8, 16, 32, 64] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True) rbf_scores, linear_scores, dimension = runTreeBasedSelection(X_train, X_test, y_train, y_test, comp_range) draw(comp_range, rbf_scores, dimension, 'rbf') draw(comp_range, linear_scores, dimension, 'linear')
def main(): comp_range = [50, 500] X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=False) for kernel in ['linear', 'poly', 'rbf', 'sigmoid', 'cosine']: print("kernel: %s" % (kernel)) runPCA(X_train, X_test, comp_range, kernel)
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from sklearn.svm import SVC import sys sys.path.append("..") from processData import loadDataDivided import SVMmodel X_train, X_test, y_train, y_test = loadDataDivided(ifSubDir=True) X_train_size = X_train.shape[0] lr = 0.001 epochs = 1000 batch_size = 256 display_step = 1 n_input = 2048 weights = None biases = None data_pointer = 0 def getNextBatch(batch_size): global data_pointer if data_pointer + batch_size <= X_train_size: data_pointer += batch_size return X_train[data_pointer - batch_size:data_pointer], y_train[ data_pointer - batch_size:data_pointer] else: return X_train[data_pointer:], y_train[data_pointer:]