luke
/
OpenRAM
mirror of https://github.com/VLSIDA/OpenRAM.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Made process a required feature. Fixed issue with features that have the same max and min

This commit is contained in:
Hunter Nichols 2020-12-17 14:08:45 -08:00
parent d6177b34f0
commit b760656572
1 changed files with 36 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

47
compiler/characterizer/analytical_util.py
View File

@ -12,7 +12,13 @@ import math
import numpy as np
import os
process_transform = {'SS':0.0, 'TT': 0.5, 'FF':1.0}
def get_data_names(file_name):
"""
Returns just the data names in the first row of the CSV
"""
with open(file_name, newline='') as csvfile:
csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
row_iter = 0
@ -22,26 +28,39 @@ def get_data_names(file_name):
return row[0].split(',')
def get_data(file_name):
"""
Returns data in CSV as lists of features
"""
with open(file_name, newline='') as csvfile:
csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
row_iter = 0
for row in csv_reader:
#data = [int(csv_str) for csv_str in ', '.join(row)]
row_iter += 1
if row_iter == 1:
feature_names = row[0].split(',')
input_list = [[] for _ in feature_names]
scaled_list = [[] for _ in feature_names]
#label_list = []
try:
process_ind = feature_names.index('process')
except:
debug.error('Process not included as a feature.')
continue
#print(row[0])
data = [float(csv_str) for csv_str in row[0].split(',')]
data = []
split_str = row[0].split(',')
for i in range(len(split_str)):
if i == process_ind:
data.append(process_transform[split_str[i]])
else:
data.append(float(split_str[i]))
data[0] = math.log(data[0], 2)
#input_list.append(data)
for i in range(len(data)):
input_list[i].append(data[i])
#label_list.append([data[-1]])
#print(data)
return input_list
def apply_samples_to_data(all_data, algo_samples):
@ -81,7 +100,7 @@ def squared_error(list_a, list_b):
def get_max_min_from_datasets(dir):
if not os.path.isdir(dir):
print("Input Directory not found:",dir)
debug.warning("Input Directory not found:{}".format(dir))
return [], [], []
# Assuming all files are CSV
@ -210,8 +229,10 @@ def get_scaled_data(file_name):
# Data is scaled by max/min and data format is changed to points vs feature lists
self_scaled_data = scale_data_and_transform(all_data)
print(self_scaled_data)
samples = np.asarray(self_scaled_data)
print(samples)
features, labels = samples[:, :-1], samples[:,-1:]
return features, labels
@ -224,8 +245,12 @@ def scale_data_and_transform(data):
for feature_list in data:
max_val = max(feature_list)
min_val = min(feature_list)
for i in range(len(feature_list)):
scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
if max_val == min_val:
scaled_data[i] = [0.0 for _ in range(len(feature_list))]
else:
for i in range(len(feature_list)):
scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
return scaled_data
def scale_input_datapoint(point, file_path):