code: Minor corrections for HUE
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61d4951280
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cdb1b01095
@ -46,15 +46,15 @@ def main(args):
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# The y axis
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# The y axis
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plt.ylabel('Mean absolute error') # Set y axis label.
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plt.ylabel('Mean absolute error') # Set y axis label.
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# plt.yscale('log')
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# plt.yscale('log')
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plt.ylim(0, 8)
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# plt.ylim(0, 8)
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# Bar offset
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# Bar offset
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x_offset = -(bar_width/2)*(n - 1)
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x_offset = -(bar_width/2)*(n - 1)
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mae_u = np.zeros(len(lmdks_pct))
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mae_u = np.zeros(len(lmdks_pct))
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mae_s = np.zeros(len(lmdks_pct))
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mae_s = np.zeros(len(lmdks_pct))
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mae_a = np.zeros(len(lmdks_pct))
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mae_a = np.zeros(len(lmdks_pct))
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mae_evt = np.zeros(len(lmdks_pct))
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mae_evt = 0
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mae_usr = np.zeros(len(lmdks_pct))
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mae_usr = 0
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for i, pct in enumerate(lmdks_pct):
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for i, pct in enumerate(lmdks_pct):
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# Find landmarks
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# Find landmarks
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@ -71,7 +71,7 @@ def main(args):
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# lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_u)
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# lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_u)
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mae_u[i] += lmdk_bgt.mae_cons(seq, rls_data_u)/args.iter
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mae_u[i] += lmdk_bgt.mae_cons(seq, rls_data_u)/args.iter
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# # Adaptive
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# Adaptive
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rls_data_a, _, _ = lmdk_bgt.adaptive_cons(seq, lmdks, epsilon, .5, .5)
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rls_data_a, _, _ = lmdk_bgt.adaptive_cons(seq, lmdks, epsilon, .5, .5)
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mae_a[i] += lmdk_bgt.mae_cons(seq, rls_data_a)/args.iter
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mae_a[i] += lmdk_bgt.mae_cons(seq, rls_data_a)/args.iter
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@ -79,26 +79,24 @@ def main(args):
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# Calculate once
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# Calculate once
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if i == 0:
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if i == 0:
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rls_data_evt, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[0]], epsilon)
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rls_data_evt, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[0]], epsilon)
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mae_evt[i] += lmdk_bgt.mae_cons(seq, rls_data_evt)/args.iter
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mae_evt += lmdk_bgt.mae_cons(seq, rls_data_evt)/args.iter
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# User
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# User
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# Calculate once
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# Calculate once
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if i == 0:
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if i == 0:
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rls_data_usr, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[len(lmdks_th)-1]], epsilon)
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rls_data_usr, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[len(lmdks_th)-1]], epsilon)
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mae_usr[i] += lmdk_bgt.mae_cons(seq, rls_data_usr)/args.iter
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mae_usr += lmdk_bgt.mae_cons(seq, rls_data_usr)/args.iter
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plt.plot(
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plt.axhline(
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x_i,
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y = mae_evt,
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mae_evt,
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linewidth=lmdk_lib.line_width
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linewidth=lmdk_lib.line_width
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)
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)
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plt.text(x_i[-1], mae_evt[-1], ' event')
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plt.text(x_i[-1], mae_evt, ' event')
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plt.plot(
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plt.axhline(
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x_i,
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y = mae_usr,
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mae_usr,
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linewidth=lmdk_lib.line_width
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linewidth=lmdk_lib.line_width
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)
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)
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plt.text(x_i[-1], mae_usr[-1], ' user')
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plt.text(x_i[-1], mae_usr, ' user')
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plt.bar(
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plt.bar(
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x_i + x_offset,
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x_i + x_offset,
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@ -443,7 +443,7 @@ def adaptive_cons(seq, lmdks, epsilon, inc_rt, dec_rt):
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else:
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else:
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skipped += 1
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skipped += 1
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# Skip current release and approximate with previous
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# Skip current release and approximate with previous
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rls_data[i] = rls_data[i - 1]
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rls_data[i] = [p[0], rls_data[i - 1][1]]
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if is_landmark:
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if is_landmark:
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# Allocate the current budget to the following releases uniformly
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# Allocate the current budget to the following releases uniformly
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for j in range(i + 1, len(seq)):
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for j in range(i + 1, len(seq)):
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@ -538,7 +538,7 @@ def skip_cons(seq, lmdks, epsilon):
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if is_landmark:
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if is_landmark:
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if i > 0:
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if i > 0:
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# Approximate with previous
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# Approximate with previous
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o = rls_data[i - 1]
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o[1] = rls_data[i - 1][1]
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bgts[i] = 0
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bgts[i] = 0
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rls_data[i] = o
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rls_data[i] = o
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return rls_data, bgts
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return rls_data, bgts
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