- Timestamp:
- 07/17/15 17:29:05 (9 years ago)
- File:
-
- 1 edited
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trunk/bin/graph
r11 r12 105 105 pass 106 106 107 print('tsval diff mean: %f' % numpy.mean(differences(db, 'long', 'tsval'))) 108 print('tsval null diff mean: %f' % numpy.mean(null_differences(db, 'long', 'tsval'))) 109 print('tsval diff weighted mean: %f' % tsvalwmean(db.subseries('train','long')+db.subseries('test','long'))) 110 print('tsval null diff weighted mean: %f' % tsvalwmean(db.subseries('train_null','long'))) 107 #print('tsval diff mean: %f' % numpy.mean(differences(db, 'long', 'tsval'))) 108 #print('tsval null diff mean: %f' % numpy.mean(null_differences(db, 'long', 'tsval'))) 109 #print('tsval diff weighted mean: %f' % tsvalwmean(db.subseries('train','long')+db.subseries('test','long'))) 110 #print('tsval null diff weighted mean: %f' % tsvalwmean(db.subseries('train_null','long'))) 111 112 113 def getTCPTSPrecision(): 114 cursor = db.conn.cursor() 115 query="""SELECT tcpts_mean FROM meta;""" 116 cursor.execute(query) 117 row = cursor.fetchone() 118 if row: 119 return row[0] 120 return None 121 122 123 def tsFilteredHistogram(): 124 tcpts_precision = getTCPTSPrecision() 125 126 num_bins = 500 127 all = db.subseries('train','long')+db.subseries('test','long') 128 diffs = [s['unusual_packet']-s['other_packet'] for s in all] 129 ts0_diffs = [s['unusual_packet']-s['other_packet'] for s in all if s['unusual_tsval']-s['other_tsval'] == 0] 130 ts1_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(s['unusual_tsval']-s['other_tsval']) > 0] 131 ts2_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(round((s['unusual_tsval']-s['other_tsval'])/tcpts_precision)) <= 1.0] 132 133 ts_mode = statistics.mode([s['unusual_tsval'] for s in all]+[s['other_tsval'] for s in all]) 134 ts_diff_mode = statistics.mode([s['unusual_tsval']-s['other_tsval'] for s in all]) 135 ts_common_mode = [s['unusual_packet']-s['other_packet'] for s in all if s['unusual_tsval']<=ts_mode and s['other_tsval']<=ts_mode] 136 ts_common_diff_mode = [s['unusual_packet']-s['other_packet'] for s in all if s['unusual_tsval']-s['other_tsval']==ts_diff_mode] 137 138 print('packet_rtt diff quadsummary: %f' % quadsummary(diffs)) 139 print('packet_rtt tsval diff=0 quadsummary: %f' % quadsummary(ts0_diffs)) 140 print('packet_rtt tsval diff>0 quadsummary: %f' % quadsummary(ts1_diffs)) 141 print('packet_rtt tsval diff<=1 quadsummary: %f' % quadsummary(ts2_diffs)) 142 print('packet_rtt tsval mode quadsummary: %f' % quadsummary(ts_common_mode)) 143 print(len(diffs), len(ts0_diffs)+len(ts1_diffs)) 144 diffs.sort() 145 cut_off_low = diffs[int(len(diffs)*0.005)] 146 cut_off_high = diffs[int(len(diffs)*0.995)] 147 148 plt.clf() 149 # the histogram of the data 150 n, bins, patches = plt.hist(diffs, num_bins, normed=0, color='black', histtype='step', alpha=0.8, 151 range=(cut_off_low,cut_off_high), label='all') 152 n, bins, patches = plt.hist(ts0_diffs, num_bins, normed=0, color='blue', histtype='step', alpha=0.8, 153 range=(cut_off_low,cut_off_high), label='tsval diff=0') 154 n, bins, patches = plt.hist(ts1_diffs, num_bins, normed=0, color='red', histtype='step', alpha=0.8, 155 range=(cut_off_low,cut_off_high), label='tsval diff>0') 156 n, bins, patches = plt.hist(ts2_diffs, num_bins, normed=0, color='orange', histtype='step', alpha=0.8, 157 range=(cut_off_low,cut_off_high), label='tsval diff<=1') 158 #n, bins, patches = plt.hist(ts_common_mode, num_bins, normed=0, color='green', histtype='step', alpha=0.8, 159 # range=(cut_off_low,cut_off_high), label='tsval common mode') 160 n, bins, patches = plt.hist(ts_common_diff_mode, num_bins, normed=0, color='green', histtype='step', alpha=0.8, 161 range=(cut_off_low,cut_off_high), label='tsval common diff mode') 162 plt.xlabel('RTT Difference') 163 plt.ylabel('Probability') 164 plt.title(r'Histogram - distribution of differences by tsval') 165 166 # Tweak spacing to prevent clipping of ylabel 167 plt.subplots_adjust(left=0.15) 168 plt.legend() 169 plt.show() 170 #plt.savefig('paper/graphs/dists-vs-dist-of-diffs2.svg') 171 172 tsFilteredHistogram() 173 sys.exit(0) 174 175 176 from pykalman import KalmanFilter 177 #kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]]) 178 kf = KalmanFilter(transition_matrices = [[1, 0], [0, 1]], n_dim_obs=2, observation_matrices = [[1.0, 0], [0, 1.0]]) 179 180 #delta = 1e-5 181 #trans_cov = delta / (1 - delta) * np.eye(2) 182 183 #kf = KalmanFilter(n_dim_obs=2, n_dim_state=2, 184 # initial_state_mean=np.zeros(2), 185 # initial_state_covariance=np.ones((2, 2)), 186 # transition_matrices=np.eye(2), 187 # observation_matrices=obs_mat, 188 # observation_covariance=1.0, 189 # transition_covariance=trans_cov) 190 191 192 #measurements = numpy.asarray([[1,0], [0,0], [0,1]]) # 3 observations 193 measurements = numpy.asarray([(s['unusual_packet'],s['other_packet']) for s in (db.subseries('train','long')+db.subseries('test','long'))]) 194 kf = kf.em(measurements, n_iter=5) 195 #(filtered_state_means, filtered_state_covariances) = kf.filter(measurements) 196 #print("packet_rtt pykalman:", filtered_state_means[-1][0]-filtered_state_means[-1][1]) 197 #print("packet_rtt pykalman:", filtered_state_means[-1]) 198 199 (smoothed_state_means, smoothed_state_covariances) = kf.smooth(measurements) 200 #up = numpy.mean([m[0] for m in smoothed_state_means]) 201 #op = numpy.mean([m[1] for m in smoothed_state_means]) 202 print("packet_rtt pykalman:", smoothed_state_means[-1], smoothed_state_means[-1][0]-smoothed_state_means[-1][1]) 203 #print("packet_rtt pykalman:", up, op, up-op) 111 204 112 205
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