163 lines
4.4 KiB
Python
Executable File
163 lines
4.4 KiB
Python
Executable File
#!/bin/env python
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__author__ = 'dongyun.zdy'
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import math
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import numpy as np
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from scipy.optimize import leastsq
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from scipy.optimize import curve_fit
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import sys
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from lmfit import Model
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import getopt
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def mg_model_form(args,
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Tstartup,
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Trow_once,
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Tres_once,
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Taggr_prepare_result,
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Taggr_process,
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Tgroup_hash_col,
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Tcopy_col
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):
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(
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Nrow_input,
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Nrow_res,
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Ncol_input,
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Ncol_aggr,
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Ncol_group
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) = args
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total_cost = Tstartup + Nrow_res * Tres_once + Nrow_input * Trow_once
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#cost for judge group
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total_cost += Nrow_input * Ncol_group * Tgroup_hash_col
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#cost for group related operation
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total_cost += Nrow_res * (Ncol_input * Tcopy_col)
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total_cost += Nrow_res * (Ncol_aggr * Taggr_prepare_result)
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#cost for input row process
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total_cost += Nrow_input * (Ncol_aggr * Taggr_process)
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return total_cost
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eval_count = 0
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def mg_model_arr(arg_sets,
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Tstartup,
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Trow_once,
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Tres_once,
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Taggr_prepare_result,
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Taggr_process,
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Tgroup_hash_col,
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Tcopy_col
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) :
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res = [mg_model_form(single_arg_set,
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Tstartup,
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Trow_once,
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Tres_once,
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Taggr_prepare_result,
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Taggr_process,
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Tgroup_hash_col,
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Tcopy_col
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) for single_arg_set in arg_sets]
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global eval_count
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eval_count += 1
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print "eval "+ str(eval_count)
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return np.array(res)
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mg_model = Model(mg_model_arr)
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mg_model.set_param_hint("Tstartup", min=0.0)
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mg_model.set_param_hint("Trow_once", min=0.0)
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mg_model.set_param_hint("Tres_once", min=0.0)
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mg_model.set_param_hint("Taggr_prepare_result", min=0.0)
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mg_model.set_param_hint("Taggr_process", min=0.0)
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mg_model.set_param_hint("Tgroup_hash_col", min=0.0)
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mg_model.set_param_hint("Tcopy_col", min=0.0)
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def extract_info_from_line(line):
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splited = line.split(",")
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line_info = []
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for item in splited:
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line_info.append(float(item))
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return line_info
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if __name__ == '__main__':
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file_name = "scan_model.res.formal.prep"
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out_file_name = "scan_model.fit"
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output_fit_res = False
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wrong_arg = False
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opts,args = getopt.getopt(sys.argv[1:],"i:o:")
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for op, value in opts:
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if "-i" == op:
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file_name = value
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elif "-o" == op:
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output_fit_res = True
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out_file_name = value
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else:
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wrong_arg = True
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if wrong_arg:
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print "wrong arg"
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sys.exit(1)
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file = open(file_name, "r")
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arg_sets = []
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times = []
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case_params = []
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for line in file:
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case_param = extract_info_from_line(line)
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case_params.append(case_param)
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# Nrow_input,
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# Nrow_res,
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# Ncol_input,
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# Ncol_aggr,
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# Ncol_group
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arg_sets.append((case_param[0],
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case_param[5],
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case_param[4],
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case_param[2],
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case_param[3]
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))
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times.append(case_param[6])
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file.close()
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arg_sets_np = np.array(arg_sets)
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times_np = np.array(times)
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#10, 0.20406430879623488, 0.016618100054245379, 14.0, 4.5, 37.0, -0.005, 0.5, -7.0
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result = mg_model.fit(times_np, arg_sets=arg_sets_np,
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Tstartup = 0.1,
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Trow_once = 0.1,
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Tres_once = 0.1,
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Taggr_prepare_result = 0.1,
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Taggr_process = 0.1,
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Tgroup_hash_col = 0.1,
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Tcopy_col = 0.1
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)
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res_line = str(result.best_values["Tstartup"]) + ","
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res_line += str(result.best_values["Trow_once"]) + ","
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res_line += str(result.best_values["Tres_once"]) + ","
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res_line += str(result.best_values["Taggr_prepare_result"]) + ","
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res_line += str(result.best_values["Taggr_process"]) + ","
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res_line += str(result.best_values["Tgroup_hash_col"]) + ","
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res_line += str(result.best_values["Tcopy_col"])
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print result.fit_report()
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if output_fit_res:
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out_file = open(out_file_name, "w")
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out_file.write(res_line)
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out_file.close()
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