Backup.

prrt/SearchEvaluation.py

-import numpy as np
-import pandas as pd
-import prrt
-from prrt.HecSearch import HECFullSearch
-from prrt.HecSearch import HECGreadySearch
-import time
-
-def evaluate(searchAlgorithm, appParams, channelParams, systemParams):
-	n_p_min = 1
-	n_p_max = get_n_p_max(channelParams.rtt_prop_fwd, channelParams.pkt_length, channelParams.data_rate_btl_fwd)
-
-	if searchAlgorithm == "FullSearch":
-		full_search = HECFullSearch(n_p_min, n_p_max, appParams, channelParams, systemParams)
-		start = time.now()
-		full_search_result = full_search.search()
-		duration = time.now() - start
-
-		return [full_search_result, duration]
-
-	if searchAlgorithm == "GreedySearch":
-		greedy_search = HECGreadySearch(n_p_min, n_p_max, appParams, channelParams, systemParams)
-		start = time.now()
-		greedy_search_result = greedy_search.search()
-		duration = time.now() - start
-
-		return [greedy_search_result, duration]
-
-
-def test_case(csv_file_path):
-
-	appParams = prrt.PrrtApplicationParameters(max_latency, max_residual_loss_rate, data_rate)
-	chnlParams = prrt.PrrtChannelParameters(...)
-	sysParams = prrt.PrrtSystemParameters(...)
-
-	for searchAlgorithm in ["FullSearch", "GreedySearch"]:
-		config, duration = evaluate(searchAlgorithm, appParams, chnlParams, sysParams)
-		rows.append(..., search, config, duration)
-
-	pd.DataFrame(rows).to_csv()
-
-def get_n_p_max(rtt_prop_fwd, pkt_length, data_rate_btl_fwd):
-	return rtt_prop_fwd * data_rate_btl_fwd / pkt_length
\ No newline at end of file

prrt/ric.pyx

-import pyximport; pyximport.install()
-import itertools
-
-def generate_restricted_integer_compositions(redundancy, positions, min, max):
-    cdef c_redundancy = redundancy
-    cdef c_positions = positions
-    cdef c_min = min
-    cdef c_max = max
-
-    if c_positions < 1:
-        raise StopIteration
-    if c_positions == 1:
-        if c_redundancy >= c_min and c_redundancy <= c_max:
-            yield (c_redundancy,)
-        raise StopIteration
-
-    for i in range(c_min, c_redundancy + 1):
-        for result in generate_restricted_integer_compositions(c_redundancy - i, c_positions - 1, i, c_max):
-            if (i <= c_max):
-                yield (i,) + result
-
-# is_order_ascending = False for full search and True for greedy search
-def gen_repair_schedules(redundancy, positions, min, max, is_order_ascending):
-    arbitrary_schedules = []
-    ordered_schedules = set()
-
-    gen_rics = generate_restricted_integer_compositions(redundancy, positions, min, max)
-    for ric in gen_rics:
-        arbitrary_schedules.append(ric)
-
-    if not is_order_ascending:
-        for ric in arbitrary_schedules:
-            ordered_schedules.add(itertools.permutations(ric))
-        return list(ordered_schedules)
-    else:
-        return arbitrary_schedules
-
-
-def gen_repair_schedule(redundancy, positions, min, max):
-	if(redundancy < positions * min or min > max):
-		raise Exception("Illegal input combinations. Make sure the min > max. And, number of total redundancy is greater that positions*min.")
-	opt_schedule = [min for p in range(positions)]
-	redundancy_left_over = redundancy - min * positions
-	last_index = positions - 1
-	while(redundancy_left_over > 0):
-		if(opt_schedule[last_index] < max):
-			opt_schedule[last_index] += 1
-			redundancy_left_over -= 1
-		else:
-			last_index -= 1
-
-	return opt_schedule
\ No newline at end of file

prrt/search_evaluation.py

@@ -4,7 +4,7 @@ import prrt
 import time
 import numpy as np
 
-dataset_file_path = 'in_12_param_4_sz_mini_1000.csv'
+dataset_file_path = 'documents/in_12_param_4_sz_mini_1000.csv'
 
 
 def get_n_p_max(rtt_prop_fwd, pkt_length, data_rate_btl_fwd):