def menu(): while (True): print("\nWhat algorithm you want to launch?") print('1. FCFS') print('2. SJF') print('3. Priority') print('4. RoundRobin') z = input('(1-4): ') print() if z == '1': fcfs = FCFS(list_processes) fcfs.run() elif z == '2': sjf = SJF(list_processes) sjf.run() elif z == '3': pr = Priority(list_processes) pr.run() elif z == '4': quantumn_time = float(input('Quantumn time: ')) rr = RoundRobin(list_processes, quantumn_time) rr.run() else: print("Wrong input") pass
class Classifier: #constructor def __init__(self): self.assessments = Assessments() self.authority = Authority() self.causation = Causation() self.conditionals = Conditionals() self.contrast = Contrast() self.difficulty = Difficulty() self.doubt = Doubt() self.emphasis = Emphasis() self.generalization = Generalization() self.inconsistency = Inconsistency() self.inyourshoes = Inyourshoes() self.necessity = Necessity() self.possibility = Possibility() self.priority = Priority() self.rhetoricalquestion = Rhetoricalquestion() self.structure = Structure() self.wants = Wants() def list_categories_names(self): return ["Assessments","Authority","Causation","Conditionals","Contrast","Difficulty","Doubt","Emphasis","Generalization","Inconsistency","Inyourshoes","Necessity","Possibility","Priority","Rhetoricalquestion","Structure","Wants"] # function to count Assessments n-grams from text def analyse(self,text): result = [] # counter of ngrams matched ngrams = 0 # Analyse the text for each category result.append(self.assessments.analyse(text)) result.append(self.authority.analyse(text)) result.append(self.causation.analyse(text)) result.append(self.conditionals.analyse(text)) result.append(self.contrast.analyse(text)) result.append(self.difficulty.analyse(text)) result.append(self.doubt.analyse(text)) result.append(self.emphasis.analyse(text)) result.append(self.generalization.analyse(text)) result.append(self.inconsistency.analyse(text)) result.append(self.inyourshoes.analyse(text)) result.append(self.necessity.analyse(text)) result.append(self.possibility.analyse(text)) result.append(self.priority.analyse(text)) result.append(self.rhetoricalquestion.analyse(text)) result.append(self.structure.analyse(text)) result.append(self.wants.analyse(text)) #normalize all categories count by percentage ngrams = sum(i for i in result) result = list(map((lambda x: round(float(x)/ngrams,5)), result)) return result
def find_most_anamalous_records_given_streams(self,cur_anamalous_streams,stream_ids, location_ids, counts, baselines,distribution_type, type_of_test): print sys._getframe().f_code.co_name aggregate_counts, aggregate_baselines = self.aggregate_counts_baselines_across_streams(cur_anamalous_streams, stream_ids,location_ids,counts,baselines) pr = Priority() priorities_locations = pr.calculate_priority(aggregate_counts,aggregate_baselines,distribution_type, type_of_test) priorities_locations_df = pd.DataFrame(priorities_locations) location_ids_df = pd.DataFrame(location_ids) aggregate_counts_df = pd.DataFrame(aggregate_counts) aggregate_baselines_df = pd.DataFrame(aggregate_baselines) frame = [location_ids_df,priorities_locations_df,aggregate_counts_df,aggregate_baselines_df] final_frame = pd.concat(frame , axis =1 ) #print final_frame final_frame.columns = ['locations' , 'priorities' , 'counts', 'baselines'] final_frame= final_frame.sort(['priorities'], ascending=[False]) print final_frame max_f_score = None max_score_index = None counts= np.array(final_frame['counts']) baselines = np.array(final_frame['baselines']) locations = np.array(final_frame['locations']) priorities = np.array(final_frame['priorities']) print counts,baselines,locations,priorities # This for loop scans over all the subsets containing top-j priority records and compute their score F(S) for i in range(1,len(final_frame)): sfn=ScoringFunctions() f_score_i = sfn.f_score_statistic_subset_aggregation(counts[0:i], baselines[0:i], distribution_type, type_of_test) print "Fscore per records subset for a set of streams" print f_score_i if max_f_score == None: max_f_score = f_score_i max_score_index = i elif max_f_score < f_score_i : max_f_score = f_score_i max_score_index = i else: max_f_score = max_f_score max_score_index = max_score_index most_anamalous_subset = [] for i in range(0, max_score_index): print final_frame['locations'][i] most_anamalous_subset.append(final_frame['locations'][i]) print "anamalous records" print most_anamalous_subset return most_anamalous_subset,max_f_score
def __init__(self): self.assessments = Assessments() self.authority = Authority() self.causation = Causation() self.conditionals = Conditionals() self.contrast = Contrast() self.difficulty = Difficulty() self.doubt = Doubt() self.emphasis = Emphasis() self.generalization = Generalization() self.inconsistency = Inconsistency() self.inyourshoes = Inyourshoes() self.necessity = Necessity() self.possibility = Possibility() self.priority = Priority() self.rhetoricalquestion = Rhetoricalquestion() self.structure = Structure() self.wants = Wants()
def menu(): print("\nWhat algorithm you want to launch?") print('1. FCFS') print('2. SJF') print('3. Priority') print('4. End') z = int(input('(1-4):')) if z == 1: fcfs = FCFS(words) fcfs.run() elif z == 2: sjf = SJF(words) sjf.run() elif z == 3: pr = Priority(words) pr.run() elif z == 4: pass else: print("Wrong input") menu()
def find_most_anamalous_streams_given_records(self, cur_anamalous_locations ,counts,baselines,stream_ids,location_ids,distribution_type, type_of_test): print sys._getframe().f_code.co_name stream_counts , stream_baselines = self.aggregate_counts_baselines_over_the_records(cur_anamalous_locations,stream_ids,location_ids,counts,baselines) pr=Priority() priorities_streams = pr.calculate_priority(stream_counts,stream_baselines,distribution_type, type_of_test) priorities_streams_df = pd.DataFrame(priorities_streams) stream_ids_df = pd.DataFrame(stream_ids) stream_counts_df = pd.DataFrame(stream_counts) stream_baselines_df = pd.DataFrame(stream_baselines) frame = [stream_ids_df,priorities_streams_df,stream_counts_df,stream_baselines_df] final_frame = pd.concat(frame , axis =1 ) final_frame.columns = ['stream_ids' , 'priorities' , 'stream_counts','stream_baselines'] final_frame= final_frame.sort(['priorities'], ascending=[False]) counts= np.array(final_frame['stream_counts']) baselines = np.array(final_frame['stream_baselines']) streams = np.array(final_frame['stream_ids']) priorities = np.array(final_frame['priorities']) max_f_score = None max_score_index = None # This for loop scans over all the subsets containing top-j priority records and compute their score F(S) for i in range(0,len(final_frame)): sfn=ScoringFunctions() f_score_i = sfn.f_score_statistic(counts[0:i], baselines[0:i], distribution_type, type_of_test) if max_f_score == None: max_f_score = f_score_i max_score_index = i elif max_f_score < f_score_i : max_f_score = f_score_i max_score_index = i else: max_f_score = max_f_score max_score_index = max_score_index most_anamalous_subset = [] for i in range(0, max_score_index): most_anamalous_subset.append(final_frame['stream_ids'][i]) print "Most anamalous streams" print most_anamalous_subset return most_anamalous_subset,max_f_score
def __init__(self, tree_structure: TreeStructure, deterministic: bool = True): """ Constructor Parameters ---------- tree_structure TreeStructure """ self.tree: TreeStructure = tree_structure self.to_replace: int = 2 self.deterministic = deterministic priority_size: int = (self.tree.number_of_ancestors * (self.tree.gene_number + 500)) * 2 self.priorities: List[Priority] = [ Priority(priority_size) for _ in range(163) ]
PID_need_reset = False oldValue = 0.0 integral = 0.0 sche = None preemption = False scheMethod = 'Priority' if scheMethod == 'EDF': sche = EDF(preemption) elif scheMethod == 'FIFO': sche = FIFO() elif scheMethod == 'Priority': sche = Priority(preemption) elif scheMethod == 'RoundRobin': sche = RoundRobin() def applyPID(yellow_line_angle): global PID_need_reset global oldValue global integral if PID_need_reset: oldValue = yellow_line_angle integral = 0.0 PID_need_reset = False sign = lambda x: math.copysign(1, x) if sign(yellow_line_angle) != sign(oldValue):
from fcfs import fcfs from Priority import Priority from RR import RR from sjf import sjf n=int(input("Enter the number of processes: ")) proc=[] for i in range(n): print("Process: ",i+1,":--") b=int(input("\tEnter Burst time: ")) a=int(input("\tEnter arrival time: ")) p=int(input("\tEnter priority: ")) proc.append([i+1,b,a,p]) quantum =int(input("Enter the Quantum: ")) f=fcfs() p=Priority() r=RR() s=sjf() print("\t\t:: Scheduling Algorithm ::") print("<===============================================>") print("FCFS:") f.findavgTime(proc,n) print("<===============================================>") print("\nSJF:") s.findavgTime(proc, n) print("<===============================================>") print("\nRR:") r.findavgTime(proc, n,quantum) print("<===============================================>") print("\nPriority:") p.priorityScheduling(proc, n)