mirror of https://github.com/VLSIDA/OpenRAM.git
Fix carriage return
This commit is contained in:
Eren Dogan
parent
e3fe8c3229
commit
03422be48c
|
|
@ -1,325 +1,325 @@
|
||||||
#
|
#
|
||||||
# Copyright (c) 2016-2019 Regents of the University of California and The Board
|
# Copyright (c) 2016-2019 Regents of the University of California and The Board
|
||||||
# of Regents for the Oklahoma Agricultural and Mechanical College
|
# of Regents for the Oklahoma Agricultural and Mechanical College
|
||||||
# (acting for and on behalf of Oklahoma State University)
|
# (acting for and on behalf of Oklahoma State University)
|
||||||
# All rights reserved.
|
# All rights reserved.
|
||||||
#
|
#
|
||||||
|
|
||||||
import debug
|
import debug
|
||||||
|
|
||||||
import csv
|
import csv
|
||||||
import math
|
import math
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import os
|
import os
|
||||||
|
|
||||||
process_transform = {'SS':0.0, 'TT': 0.5, 'FF':1.0}
|
process_transform = {'SS':0.0, 'TT': 0.5, 'FF':1.0}
|
||||||
|
|
||||||
def get_data_names(file_name, exclude_area=True):
|
def get_data_names(file_name, exclude_area=True):
|
||||||
"""
|
"""
|
||||||
Returns just the data names in the first row of the CSV
|
Returns just the data names in the first row of the CSV
|
||||||
"""
|
"""
|
||||||
|
|
||||||
with open(file_name, newline='') as csvfile:
|
with open(file_name, newline='') as csvfile:
|
||||||
csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
|
csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
|
||||||
row_iter = 0
|
row_iter = 0
|
||||||
# reader is iterable not a list, probably a better way to do this
|
# reader is iterable not a list, probably a better way to do this
|
||||||
for row in csv_reader:
|
for row in csv_reader:
|
||||||
# Return names from first row
|
# Return names from first row
|
||||||
names = row[0].split(',')
|
names = row[0].split(',')
|
||||||
break
|
break
|
||||||
if exclude_area:
|
if exclude_area:
|
||||||
try:
|
try:
|
||||||
area_ind = names.index('area')
|
area_ind = names.index('area')
|
||||||
except ValueError:
|
except ValueError:
|
||||||
area_ind = -1
|
area_ind = -1
|
||||||
|
|
||||||
if area_ind != -1:
|
if area_ind != -1:
|
||||||
names = names[:area_ind] + names[area_ind+1:]
|
names = names[:area_ind] + names[area_ind+1:]
|
||||||
return names
|
return names
|
||||||
|
|
||||||
def get_data(file_name):
|
def get_data(file_name):
|
||||||
"""
|
"""
|
||||||
Returns data in CSV as lists of features
|
Returns data in CSV as lists of features
|
||||||
"""
|
"""
|
||||||
|
|
||||||
with open(file_name, newline='') as csvfile:
|
with open(file_name, newline='') as csvfile:
|
||||||
csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
|
csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
|
||||||
row_iter = 0
|
row_iter = 0
|
||||||
removed_items = 1
|
removed_items = 1
|
||||||
for row in csv_reader:
|
for row in csv_reader:
|
||||||
row_iter += 1
|
row_iter += 1
|
||||||
if row_iter == 1:
|
if row_iter == 1:
|
||||||
feature_names = row[0].split(',')
|
feature_names = row[0].split(',')
|
||||||
input_list = [[] for _ in range(len(feature_names)-removed_items)]
|
input_list = [[] for _ in range(len(feature_names)-removed_items)]
|
||||||
try:
|
try:
|
||||||
# Save to remove area
|
# Save to remove area
|
||||||
area_ind = feature_names.index('area')
|
area_ind = feature_names.index('area')
|
||||||
except ValueError:
|
except ValueError:
|
||||||
area_ind = -1
|
area_ind = -1
|
||||||
|
|
||||||
try:
|
try:
|
||||||
process_ind = feature_names.index('process')
|
process_ind = feature_names.index('process')
|
||||||
except:
|
except:
|
||||||
debug.error('Process not included as a feature.')
|
debug.error('Process not included as a feature.')
|
||||||
continue
|
continue
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
data = []
|
data = []
|
||||||
split_str = row[0].split(',')
|
split_str = row[0].split(',')
|
||||||
for i in range(len(split_str)):
|
for i in range(len(split_str)):
|
||||||
if i == process_ind:
|
if i == process_ind:
|
||||||
data.append(process_transform[split_str[i]])
|
data.append(process_transform[split_str[i]])
|
||||||
elif i == area_ind:
|
elif i == area_ind:
|
||||||
continue
|
continue
|
||||||
else:
|
else:
|
||||||
data.append(float(split_str[i]))
|
data.append(float(split_str[i]))
|
||||||
|
|
||||||
data[0] = math.log(data[0], 2)
|
data[0] = math.log(data[0], 2)
|
||||||
|
|
||||||
for i in range(len(data)):
|
for i in range(len(data)):
|
||||||
input_list[i].append(data[i])
|
input_list[i].append(data[i])
|
||||||
|
|
||||||
return input_list
|
return input_list
|
||||||
|
|
||||||
def apply_samples_to_data(all_data, algo_samples):
|
def apply_samples_to_data(all_data, algo_samples):
|
||||||
# Take samples from algorithm and match them to samples in data
|
# Take samples from algorithm and match them to samples in data
|
||||||
data_samples, unused_data = [], []
|
data_samples, unused_data = [], []
|
||||||
sample_positions = set()
|
sample_positions = set()
|
||||||
for sample in algo_samples:
|
for sample in algo_samples:
|
||||||
sample_positions.add(find_sample_position_with_min_error(all_data, sample))
|
sample_positions.add(find_sample_position_with_min_error(all_data, sample))
|
||||||
|
|
||||||
for i in range(len(all_data)):
|
for i in range(len(all_data)):
|
||||||
if i in sample_positions:
|
if i in sample_positions:
|
||||||
data_samples.append(all_data[i])
|
data_samples.append(all_data[i])
|
||||||
else:
|
else:
|
||||||
unused_data.append(all_data[i])
|
unused_data.append(all_data[i])
|
||||||
|
|
||||||
return data_samples, unused_data
|
return data_samples, unused_data
|
||||||
|
|
||||||
def find_sample_position_with_min_error(data, sampled_vals):
|
def find_sample_position_with_min_error(data, sampled_vals):
|
||||||
min_error = 0
|
min_error = 0
|
||||||
sample_pos = 0
|
sample_pos = 0
|
||||||
count = 0
|
count = 0
|
||||||
for data_slice in data:
|
for data_slice in data:
|
||||||
error = squared_error(data_slice, sampled_vals)
|
error = squared_error(data_slice, sampled_vals)
|
||||||
if min_error == 0 or error < min_error:
|
if min_error == 0 or error < min_error:
|
||||||
min_error = error
|
min_error = error
|
||||||
sample_pos = count
|
sample_pos = count
|
||||||
count += 1
|
count += 1
|
||||||
return sample_pos
|
return sample_pos
|
||||||
|
|
||||||
def squared_error(list_a, list_b):
|
def squared_error(list_a, list_b):
|
||||||
error_sum = 0;
|
error_sum = 0;
|
||||||
for a,b in zip(list_a, list_b):
|
for a,b in zip(list_a, list_b):
|
||||||
error_sum+=(a-b)**2
|
error_sum+=(a-b)**2
|
||||||
return error_sum
|
return error_sum
|
||||||
|
|
||||||
|
|
||||||
def get_max_min_from_datasets(dir):
|
def get_max_min_from_datasets(dir):
|
||||||
if not os.path.isdir(dir):
|
if not os.path.isdir(dir):
|
||||||
debug.warning("Input Directory not found:{}".format(dir))
|
debug.warning("Input Directory not found:{}".format(dir))
|
||||||
return [], [], []
|
return [], [], []
|
||||||
|
|
||||||
# Assuming all files are CSV
|
# Assuming all files are CSV
|
||||||
data_files = [f for f in os.listdir(dir) if os.path.isfile(os.path.join(dir, f))]
|
data_files = [f for f in os.listdir(dir) if os.path.isfile(os.path.join(dir, f))]
|
||||||
maxs,mins,sums,total_count = [],[],[],0
|
maxs,mins,sums,total_count = [],[],[],0
|
||||||
for file in data_files:
|
for file in data_files:
|
||||||
data = get_data(os.path.join(dir, file))
|
data = get_data(os.path.join(dir, file))
|
||||||
# Get max, min, sum, and count from every file
|
# Get max, min, sum, and count from every file
|
||||||
data_max, data_min, data_sum, count = [],[],[], 0
|
data_max, data_min, data_sum, count = [],[],[], 0
|
||||||
for feature_list in data:
|
for feature_list in data:
|
||||||
data_max.append(max(feature_list))
|
data_max.append(max(feature_list))
|
||||||
data_min.append(min(feature_list))
|
data_min.append(min(feature_list))
|
||||||
data_sum.append(sum(feature_list))
|
data_sum.append(sum(feature_list))
|
||||||
count = len(feature_list)
|
count = len(feature_list)
|
||||||
|
|
||||||
# Aggregate the data
|
# Aggregate the data
|
||||||
if not maxs or not mins or not sums:
|
if not maxs or not mins or not sums:
|
||||||
maxs,mins,sums,total_count = data_max,data_min,data_sum,count
|
maxs,mins,sums,total_count = data_max,data_min,data_sum,count
|
||||||
else:
|
else:
|
||||||
for i in range(len(maxs)):
|
for i in range(len(maxs)):
|
||||||
maxs[i] = max(data_max[i], maxs[i])
|
maxs[i] = max(data_max[i], maxs[i])
|
||||||
mins[i] = min(data_min[i], mins[i])
|
mins[i] = min(data_min[i], mins[i])
|
||||||
sums[i] = data_sum[i]+sums[i]
|
sums[i] = data_sum[i]+sums[i]
|
||||||
total_count+=count
|
total_count+=count
|
||||||
|
|
||||||
avgs = [s/total_count for s in sums]
|
avgs = [s/total_count for s in sums]
|
||||||
return maxs,mins,avgs
|
return maxs,mins,avgs
|
||||||
|
|
||||||
def get_max_min_from_file(path):
|
def get_max_min_from_file(path):
|
||||||
if not os.path.isfile(path):
|
if not os.path.isfile(path):
|
||||||
debug.warning("Input file not found: {}".format(path))
|
debug.warning("Input file not found: {}".format(path))
|
||||||
return [], [], []
|
return [], [], []
|
||||||
|
|
||||||
|
|
||||||
data = get_data(path)
|
data = get_data(path)
|
||||||
# Get max, min, sum, and count from every file
|
# Get max, min, sum, and count from every file
|
||||||
data_max, data_min, data_sum, count = [],[],[], 0
|
data_max, data_min, data_sum, count = [],[],[], 0
|
||||||
for feature_list in data:
|
for feature_list in data:
|
||||||
data_max.append(max(feature_list))
|
data_max.append(max(feature_list))
|
||||||
data_min.append(min(feature_list))
|
data_min.append(min(feature_list))
|
||||||
data_sum.append(sum(feature_list))
|
data_sum.append(sum(feature_list))
|
||||||
count = len(feature_list)
|
count = len(feature_list)
|
||||||
|
|
||||||
avgs = [s/count for s in data_sum]
|
avgs = [s/count for s in data_sum]
|
||||||
return data_max, data_min, avgs
|
return data_max, data_min, avgs
|
||||||
|
|
||||||
def get_data_and_scale(file_name, sample_dir):
|
def get_data_and_scale(file_name, sample_dir):
|
||||||
maxs,mins,avgs = get_max_min_from_datasets(sample_dir)
|
maxs,mins,avgs = get_max_min_from_datasets(sample_dir)
|
||||||
|
|
||||||
# Get data
|
# Get data
|
||||||
all_data = get_data(file_name)
|
all_data = get_data(file_name)
|
||||||
|
|
||||||
# Scale data from file
|
# Scale data from file
|
||||||
self_scaled_data = [[] for _ in range(len(all_data[0]))]
|
self_scaled_data = [[] for _ in range(len(all_data[0]))]
|
||||||
self_maxs,self_mins = [],[]
|
self_maxs,self_mins = [],[]
|
||||||
for feature_list, cur_max, cur_min in zip(all_data,maxs, mins):
|
for feature_list, cur_max, cur_min in zip(all_data,maxs, mins):
|
||||||
for i in range(len(feature_list)):
|
for i in range(len(feature_list)):
|
||||||
self_scaled_data[i].append((feature_list[i]-cur_min)/(cur_max-cur_min))
|
self_scaled_data[i].append((feature_list[i]-cur_min)/(cur_max-cur_min))
|
||||||
|
|
||||||
return np.asarray(self_scaled_data)
|
return np.asarray(self_scaled_data)
|
||||||
|
|
||||||
def rescale_data(data, old_maxs, old_mins, new_maxs, new_mins):
|
def rescale_data(data, old_maxs, old_mins, new_maxs, new_mins):
|
||||||
# unscale from old values, rescale by new values
|
# unscale from old values, rescale by new values
|
||||||
data_new_scaling = []
|
data_new_scaling = []
|
||||||
for data_row in data:
|
for data_row in data:
|
||||||
scaled_row = []
|
scaled_row = []
|
||||||
for val, old_max,old_min, cur_max, cur_min in zip(data_row, old_maxs,old_mins, new_maxs, new_mins):
|
for val, old_max,old_min, cur_max, cur_min in zip(data_row, old_maxs,old_mins, new_maxs, new_mins):
|
||||||
unscaled_data = val*(old_max-old_min) + old_min
|
unscaled_data = val*(old_max-old_min) + old_min
|
||||||
scaled_row.append((unscaled_data-cur_min)/(cur_max-cur_min))
|
scaled_row.append((unscaled_data-cur_min)/(cur_max-cur_min))
|
||||||
|
|
||||||
data_new_scaling.append(scaled_row)
|
data_new_scaling.append(scaled_row)
|
||||||
|
|
||||||
return data_new_scaling
|
return data_new_scaling
|
||||||
|
|
||||||
def sample_from_file(num_samples, file_name, sample_dir=None):
|
def sample_from_file(num_samples, file_name, sample_dir=None):
|
||||||
"""
|
"""
|
||||||
Get a portion of the data from CSV file and scale it based on max/min of dataset.
|
Get a portion of the data from CSV file and scale it based on max/min of dataset.
|
||||||
Duplicate samples are trimmed.
|
Duplicate samples are trimmed.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
if sample_dir:
|
if sample_dir:
|
||||||
maxs,mins,avgs = get_max_min_from_datasets(sample_dir)
|
maxs,mins,avgs = get_max_min_from_datasets(sample_dir)
|
||||||
else:
|
else:
|
||||||
maxs,mins,avgs = [], [], []
|
maxs,mins,avgs = [], [], []
|
||||||
|
|
||||||
# Get data
|
# Get data
|
||||||
all_data = get_data(file_name)
|
all_data = get_data(file_name)
|
||||||
|
|
||||||
# Get algorithms sample points, assuming hypercube for now
|
# Get algorithms sample points, assuming hypercube for now
|
||||||
num_labels = 1
|
num_labels = 1
|
||||||
inp_dims = len(all_data) - num_labels
|
inp_dims = len(all_data) - num_labels
|
||||||
samples = np.random.rand(num_samples, inp_dims)
|
samples = np.random.rand(num_samples, inp_dims)
|
||||||
|
|
||||||
|
|
||||||
# Scale data from file
|
# Scale data from file
|
||||||
self_scaled_data = [[] for _ in range(len(all_data[0]))]
|
self_scaled_data = [[] for _ in range(len(all_data[0]))]
|
||||||
self_maxs,self_mins = [],[]
|
self_maxs,self_mins = [],[]
|
||||||
for feature_list in all_data:
|
for feature_list in all_data:
|
||||||
max_val = max(feature_list)
|
max_val = max(feature_list)
|
||||||
self_maxs.append(max_val)
|
self_maxs.append(max_val)
|
||||||
min_val = min(feature_list)
|
min_val = min(feature_list)
|
||||||
self_mins.append(min_val)
|
self_mins.append(min_val)
|
||||||
for i in range(len(feature_list)):
|
for i in range(len(feature_list)):
|
||||||
self_scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
|
self_scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
|
||||||
# Apply algorithm sampling points to available data
|
# Apply algorithm sampling points to available data
|
||||||
sampled_data, unused_data = apply_samples_to_data(self_scaled_data,samples)
|
sampled_data, unused_data = apply_samples_to_data(self_scaled_data,samples)
|
||||||
|
|
||||||
#unscale values and rescale using all available data (both sampled and unused points rescaled)
|
#unscale values and rescale using all available data (both sampled and unused points rescaled)
|
||||||
if len(maxs)!=0 and len(mins)!=0:
|
if len(maxs)!=0 and len(mins)!=0:
|
||||||
sampled_data = rescale_data(sampled_data, self_maxs,self_mins, maxs, mins)
|
sampled_data = rescale_data(sampled_data, self_maxs,self_mins, maxs, mins)
|
||||||
unused_new_scaling = rescale_data(unused_data, self_maxs,self_mins, maxs, mins)
|
unused_new_scaling = rescale_data(unused_data, self_maxs,self_mins, maxs, mins)
|
||||||
|
|
||||||
return np.asarray(sampled_data), np.asarray(unused_new_scaling)
|
return np.asarray(sampled_data), np.asarray(unused_new_scaling)
|
||||||
|
|
||||||
def get_scaled_data(file_name):
|
def get_scaled_data(file_name):
|
||||||
"""Get data from CSV file and scale it based on max/min of dataset"""
|
"""Get data from CSV file and scale it based on max/min of dataset"""
|
||||||
|
|
||||||
if file_name:
|
if file_name:
|
||||||
maxs,mins,avgs = get_max_min_from_file(file_name)
|
maxs,mins,avgs = get_max_min_from_file(file_name)
|
||||||
else:
|
else:
|
||||||
maxs,mins,avgs = [], [], []
|
maxs,mins,avgs = [], [], []
|
||||||
|
|
||||||
# Get data
|
# Get data
|
||||||
all_data = get_data(file_name)
|
all_data = get_data(file_name)
|
||||||
|
|
||||||
# Data is scaled by max/min and data format is changed to points vs feature lists
|
# 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)
|
self_scaled_data = scale_data_and_transform(all_data)
|
||||||
data_np = np.asarray(self_scaled_data)
|
data_np = np.asarray(self_scaled_data)
|
||||||
return data_np
|
return data_np
|
||||||
|
|
||||||
def scale_data_and_transform(data):
|
def scale_data_and_transform(data):
|
||||||
"""
|
"""
|
||||||
Assume data is a list of features, change to a list of points and max/min scale
|
Assume data is a list of features, change to a list of points and max/min scale
|
||||||
"""
|
"""
|
||||||
|
|
||||||
scaled_data = [[] for _ in range(len(data[0]))]
|
scaled_data = [[] for _ in range(len(data[0]))]
|
||||||
for feature_list in data:
|
for feature_list in data:
|
||||||
max_val = max(feature_list)
|
max_val = max(feature_list)
|
||||||
min_val = min(feature_list)
|
min_val = min(feature_list)
|
||||||
|
|
||||||
for i in range(len(feature_list)):
|
for i in range(len(feature_list)):
|
||||||
if max_val == min_val:
|
if max_val == min_val:
|
||||||
scaled_data[i].append(0.0)
|
scaled_data[i].append(0.0)
|
||||||
else:
|
else:
|
||||||
scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
|
scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
|
||||||
return scaled_data
|
return scaled_data
|
||||||
|
|
||||||
def scale_input_datapoint(point, file_path):
|
def scale_input_datapoint(point, file_path):
|
||||||
"""
|
"""
|
||||||
Input data has no output and needs to be scaled like the model inputs during
|
Input data has no output and needs to be scaled like the model inputs during
|
||||||
training.
|
training.
|
||||||
"""
|
"""
|
||||||
maxs, mins, avgs = get_max_min_from_file(file_path)
|
maxs, mins, avgs = get_max_min_from_file(file_path)
|
||||||
debug.info(3, "maxs={}".format(maxs))
|
debug.info(3, "maxs={}".format(maxs))
|
||||||
debug.info(3, "mins={}".format(mins))
|
debug.info(3, "mins={}".format(mins))
|
||||||
debug.info(3, "point={}".format(point))
|
debug.info(3, "point={}".format(point))
|
||||||
|
|
||||||
scaled_point = []
|
scaled_point = []
|
||||||
for feature, mx, mn in zip(point, maxs, mins):
|
for feature, mx, mn in zip(point, maxs, mins):
|
||||||
if mx == mn:
|
if mx == mn:
|
||||||
scaled_point.append(0.0)
|
scaled_point.append(0.0)
|
||||||
else:
|
else:
|
||||||
scaled_point.append((feature-mn)/(mx-mn))
|
scaled_point.append((feature-mn)/(mx-mn))
|
||||||
return scaled_point
|
return scaled_point
|
||||||
|
|
||||||
def unscale_data(data, file_path, pos=None):
|
def unscale_data(data, file_path, pos=None):
|
||||||
if file_path:
|
if file_path:
|
||||||
maxs,mins,avgs = get_max_min_from_file(file_path)
|
maxs,mins,avgs = get_max_min_from_file(file_path)
|
||||||
else:
|
else:
|
||||||
debug.error("Must provide reference data to unscale")
|
debug.error("Must provide reference data to unscale")
|
||||||
return None
|
return None
|
||||||
|
|
||||||
# Hard coded to only convert the last max/min (i.e. the label of the data)
|
# Hard coded to only convert the last max/min (i.e. the label of the data)
|
||||||
if pos == None:
|
if pos == None:
|
||||||
maxs,mins,avgs = maxs[-1],mins[-1],avgs[-1]
|
maxs,mins,avgs = maxs[-1],mins[-1],avgs[-1]
|
||||||
else:
|
else:
|
||||||
maxs,mins,avgs = maxs[pos],mins[pos],avgs[pos]
|
maxs,mins,avgs = maxs[pos],mins[pos],avgs[pos]
|
||||||
unscaled_data = []
|
unscaled_data = []
|
||||||
for data_row in data:
|
for data_row in data:
|
||||||
unscaled_val = data_row*(maxs-mins) + mins
|
unscaled_val = data_row*(maxs-mins) + mins
|
||||||
unscaled_data.append(unscaled_val)
|
unscaled_data.append(unscaled_val)
|
||||||
|
|
||||||
return unscaled_data
|
return unscaled_data
|
||||||
|
|
||||||
def abs_error(labels, preds):
|
def abs_error(labels, preds):
|
||||||
total_error = 0
|
total_error = 0
|
||||||
for label_i, pred_i in zip(labels, preds):
|
for label_i, pred_i in zip(labels, preds):
|
||||||
cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
|
cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
|
||||||
total_error += cur_error
|
total_error += cur_error
|
||||||
return total_error/len(labels)
|
return total_error/len(labels)
|
||||||
|
|
||||||
def max_error(labels, preds):
|
def max_error(labels, preds):
|
||||||
mx_error = 0
|
mx_error = 0
|
||||||
for label_i, pred_i in zip(labels, preds):
|
for label_i, pred_i in zip(labels, preds):
|
||||||
cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
|
cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
|
||||||
mx_error = max(cur_error, mx_error)
|
mx_error = max(cur_error, mx_error)
|
||||||
return mx_error
|
return mx_error
|
||||||
|
|
||||||
def min_error(labels, preds):
|
def min_error(labels, preds):
|
||||||
mn_error = 1
|
mn_error = 1
|
||||||
for label_i, pred_i in zip(labels, preds):
|
for label_i, pred_i in zip(labels, preds):
|
||||||
cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
|
cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
|
||||||
mn_error = min(cur_error, mn_error)
|
mn_error = min(cur_error, mn_error)
|
||||||
return mn_error
|
return mn_error
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue