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How to use n_result_list method in avocado
Best Python code snippet using avocado_python
timed_decision_making_lib.py
Source:timed_decision_making_lib.py 
1import numpy as np2from matplotlib import pyplot as plt3import os4from numba import jit5from .. import run6fs = 10 # font size7_color_list = ['blue', 'red', 'black', 'yellow', 'pink']8plt.rcParams["font.family"] = "Helvetica"9plt.style.use('default')10SMALL_SIZE = 811MEDIUM_SIZE = 1012BIGGER_SIZE = 1213plt.rc('font', size=fs) # controls default text sizes14plt.rc('axes', titlesize=fs) # fontsize of the axes title15plt.rc('axes', labelsize=fs) # fontsize of the x and y labels16plt.rc('xtick', labelsize=fs) # fontsize of the tick labels17plt.rc('ytick', labelsize=fs) # fontsize of the tick labels18plt.rc('legend', fontsize=fs) # legend fontsize19plt.rc('figure', titlesize=fs)20class LeftAlignAvr:21 def __init__(self, max_length):22 self.value = np.zeros((max_length,))23 self.value_2 = np.zeros((max_length,))24 self.number = np.zeros((max_length,))25 self.max_length = max_length26 def add(self, add_value):27 temp_length = len(add_value)28 self.value[0:temp_length] += add_value29 self.value_2[0:temp_length] += add_value * add_value30 self.number[0:temp_length] += 131 def return_average(self):32 zero_number_idx = np.argwhere(self.number <= 1)33 self.number = np.delete(self.number, zero_number_idx)34 self.value = np.delete(self.value, zero_number_idx)35 self.value_2 = np.delete(self.value_2, zero_number_idx)36 return self.value/self.number37 def return_sem(self):38 zero_number_idx = np.argwhere(self.number <= 1)39 self.number = np.delete(self.number, zero_number_idx)40 self.value = np.delete(self.value, zero_number_idx)41 self.value_2 = np.delete(self.value_2, zero_number_idx)42 return np.sqrt((self.value_2/self.number)-(self.value/self.number)**2)/np.sqrt(self.number)43class RightAlignAvr:44 def __init__(self, max_length):45 self.value = np.zeros((max_length,))46 self.value_2 = np.zeros((max_length,))47 self.number = np.zeros((max_length,))48 self.max_length = max_length49 def add(self, add_value):50 temp_length = len(add_value)51 self.value[self.max_length-temp_length:self.max_length] += add_value52 self.value_2[self.max_length-temp_length:self.max_length] += add_value * add_value53 self.number[self.max_length-temp_length:self.max_length] += 154 def return_average(self):55 zero_number_idx = np.argwhere(self.number <= 1)56 self.number = np.delete(self.number, zero_number_idx)57 self.value = np.delete(self.value, zero_number_idx)58 self.value_2 = np.delete(self.value_2, zero_number_idx)59 return self.value/self.number60 def return_sem(self):61 zero_number_idx = np.argwhere(self.number <= 1)62 self.number = np.delete(self.number, zero_number_idx)63 self.value = np.delete(self.value, zero_number_idx)64 self.value_2 = np.delete(self.value_2, zero_number_idx)65 return np.sqrt((self.value_2/self.number)-(self.value/self.number)**2)/np.sqrt(self.number)66@jit(nopython=True)67def local_maximum_location(signal, loc):68 if loc == 0:69 for x in range(len(signal)-1):70 if signal[x] >= signal[x+1]:71 return x72 return len(signal)-173 elif loc == len(signal)-1:74 for x in range(len(signal)-1, 0, -1):75 if signal[x-1] < signal[x]:76 return x77 return 078 elif signal[loc-1] < signal[loc]:79 for x in range(loc, len(signal) - 1):80 if signal[x] >= signal[x + 1]:81 return x82 return len(signal) - 183 else:84 for x in range(loc-1, 0, -1):85 if signal[x-1] < signal[x]:86 return x87 return 088@jit(nopython=True)89def no_greater_than_loc(sorted_array, value):90 for i in range(len(sorted_array)):91 if sorted_array[i] >= value:92 return i93 return len(sorted_array)94#@jit(nopython=True)95def p_n_neuron_split(output_neurons, input_neuron, local_peak_outputs, local_peak_input):96 index_of_duplicate = np.argwhere(output_neurons==input_neuron)97 if local_peak_outputs[-1] < local_peak_input:98 delim = len(local_peak_outputs)99 else:100 for i in range(len(local_peak_outputs)):101 if local_peak_outputs[i] >= local_peak_input:102 delim = i103 break104 if len(index_of_duplicate) == 0:105 return np.arange(delim, len(output_neurons)), np.arange(0, delim)106 else:107 if index_of_duplicate[0,0]>=delim:108 return np.concatenate([np.arange(delim, index_of_duplicate[0,0]), np.arange(index_of_duplicate[0,0]+1, len(output_neurons))]), np.arange(0, delim)109 else:#index_of_duplicate[0,0]<delim110 return np.arange(delim, len(output_neurons)), np.concatenate([np.arange(0, index_of_duplicate[0,0]), np.arange(index_of_duplicate[0,0]+1, delim)])111class WeightConnection:112 def __init__(self, stim_n, func_relevance_threshold=2.):113 '''114 :param firing_rate_binder: shape (stim, time, neuron)115 :param weight_hh: shape (input_n, output_n)116 :param time_points: shape (time_n,)117 '''118 self.stim_n = stim_n119 self.func_relevance_threshold = func_relevance_threshold120 self.p_result_list = [LeftAlignAvr(100) for i in range(self.stim_n)]121 self.n_result_list = [RightAlignAvr(100) for i in range(self.stim_n)]122 def reset_parameter(self, firing_rate_binder, weight_hh, time_points):123 self.firing_rate_binder = firing_rate_binder#.astype(np.float64)124 self.weight_hh = weight_hh125 self.time_points = time_points126 self.func_relevant_neuron = firing_rate_binder>self.func_relevance_threshold127 def weight_connection(self, input_neurons_1, output_neurons_1, local_peak_inputs, local_peak_outputs, stim_idx1, stim_idx2):128 order_idx_inputs = np.argsort(local_peak_inputs)129 order_idx_outputs = np.argsort(local_peak_outputs)130 local_peak_inputs = local_peak_inputs[order_idx_inputs]131 local_peak_outputs = local_peak_outputs[order_idx_outputs]132 input_neurons = input_neurons_1[order_idx_inputs]133 output_neurons = output_neurons_1[order_idx_outputs]134 weight = self.weight_hh[input_neurons, :][:, output_neurons]135 #print(weight.shape)136 stim_diff = np.abs(stim_idx1-stim_idx2)137 for i in range(len(local_peak_inputs)):138 p_neuron, n_neuron = p_n_neuron_split(output_neurons, input_neurons[i], local_peak_outputs, local_peak_inputs[i])139 self.p_result_list[stim_diff].add(weight[i, p_neuron])140 self.n_result_list[stim_diff].add(weight[i, n_neuron])141 def do(self):142 for time_point in self.time_points:143 func_relevance_neuron = np.argwhere(np.sum(self.func_relevant_neuron[:, time_point, :], axis=0) > 0).flatten()144 firing_rate = self.firing_rate_binder[:, time_point, func_relevance_neuron]145 max_stim = np.argmax(firing_rate, axis=0)146 func_relevant_neuron_single_time = [func_relevance_neuron[np.argwhere(max_stim==stim_idx).flatten()] for stim_idx in range(self.stim_n)]147 #func_relevant_neuron_single_time = [np.argwhere(self.func_relevant_neuron[stim_idx, time_point, :]).squeeze() for stim_idx in range(self.stim_n)]148 for stim_idx1 in range(self.stim_n):149 input_neurons = func_relevant_neuron_single_time[stim_idx1]150 #print(input_neurons)151 #print([x for x in input_neurons])152 local_peak_inputs = np.array([local_maximum_location(self.firing_rate_binder[stim_idx1, :, x], time_point) for x in input_neurons]) # peak location when c = 0.01153 for stim_idx2 in range(stim_idx1, self.stim_n):154 output_neurons = func_relevant_neuron_single_time[stim_idx2]155 local_peak_outputs = np.array([local_maximum_location(self.firing_rate_binder[stim_idx2, :, x], time_point) for x in output_neurons]) # peak location when c = 0.01156 self.weight_connection(input_neurons, output_neurons, local_peak_inputs, local_peak_outputs, stim_idx1, stim_idx2)157 def return_mean(self):158 delete_num = 1159 p_mean = [x.return_average()[:-delete_num] for x in self.p_result_list]160 n_mean = [x.return_average()[delete_num:] for x in self.n_result_list]161 return p_mean, n_mean162 def return_sem(self):163 delete_num = 1164 p_sem = [x.return_sem()[:-delete_num] for x in self.p_result_list]165 n_sem = [x.return_sem()[delete_num:] for x in self.n_result_list]166 return p_sem, n_sem167def weight_connection_with_time(serial_idxes, epoch='interval', gamma_bar=np.array([1]), c=np.array([0.01, -0.01]), time_points=np.array([600]), func_relevance_threshold=2, noise_on=False):168 weight_connection_obj = WeightConnection(len(c))169 time_points = (time_points/20).astype(np.int)170 dly_interval = 1600171 prod_intervals = np.array([1200])172 prod_intervals, c = np.meshgrid(prod_intervals, c)173 prod_intervals = prod_intervals.flatten()174 c = c.flatten()175 batch_size = len(prod_intervals)176 gamma_bar = np.array([gamma_bar] * batch_size).flatten()177 dly_intervals = np.array([dly_interval] * batch_size)178 for serial_idx in serial_idxes:179 model_dir = './core/model/' + 'timed_decision_making/' + str(serial_idx)180 if not os.path.exists(model_dir):181 continue182 runnerObj = run.Runner(model_dir=model_dir, rule_name='timed_decision_making', is_cuda=False, noise_on=noise_on)183 trial_input, run_result = runnerObj.run(batch_size=batch_size, prod_interval=prod_intervals, dly_interval=dly_intervals,184 gamma_bar=gamma_bar, c=c)185 stim1_off, stim2_on = trial_input.epochs[epoch]186 firing_rate_binder = run_result.firing_rate_binder.detach().cpu().numpy()187 firing_rate_binder = np.concatenate(list(firing_rate_binder[stim1_off[i]:stim2_on[i], i, :][np.newaxis, :, :] for i in range(0, batch_size)), axis=0)188 weight_connection_obj.reset_parameter(firing_rate_binder, runnerObj.model.weight_hh.detach().cpu().numpy(), time_points)189 weight_connection_obj.do()190 p_mean, n_mean = weight_connection_obj.return_mean()191 p_std, n_std = weight_connection_obj.return_sem()192 fig = plt.figure(figsize=(2.5, 2.1))193 ax = fig.add_axes([0.3, 0.2, 0.7*0.9, 0.6])194 ax.spines['top'].set_visible(False)195 ax.spines['right'].set_visible(False)196 mean_same = np.concatenate([n_mean[0], p_mean[0]])197 std_same = np.concatenate([n_std[0], p_std[0]])198 x_coord_same = np.concatenate([-np.flip(np.arange(len(n_mean[0])), axis=0)-1, np.arange(len(p_mean[0]))])199 plt.plot(x_coord_same, mean_same, label='Same choice pref.')200 plt.fill_between(x_coord_same, mean_same-std_same, mean_same+std_same, alpha=0.5)201 mean_diff = np.concatenate([n_mean[1], p_mean[1]])202 std_diff = np.concatenate([n_std[1], p_std[1]])203 x_coord_diff = np.concatenate([-np.flip(np.arange(len(n_mean[1])), axis=0)-1, np.arange(len(p_mean[1]))])204 plt.plot(x_coord_diff, mean_diff, label='Diff. choice pref.')205 plt.fill_between(x_coord_diff, mean_diff-std_diff, mean_diff+std_diff, alpha=0.5)206 plt.gca().set_xlabel('Peak order difference', fontsize=fs)207 plt.gca().set_ylabel('Recurrent weight', fontsize=fs)208 #plt.plot([-13, 11], [0, 0], '--', color='black')209 min_x = np.min([x_coord_same[0], x_coord_diff[0]])210 max_x = np.max([x_coord_same[-1], x_coord_diff[-1]])211 plt.plot([min_x, max_x], [0, 0], '--', color='black')212 plt.xlim([min_x, max_x])213 if min_x<=-10 and max_x>=10:214 plt.xticks([-10,0,9], [-10, 1, 10], fontsize=fs)215 else:216 plt.xticks([-10+5,0,9-5], [-10+5, 1, 10-5], fontsize=fs)217 fig.legend(loc='best', fontsize=fs, frameon=False)218 plt.show()...
main.py
Source:main.py
1import jieba2import OpenHowNet3# OpenHowNet.download()4hownet_dict = OpenHowNet.HowNetDict()5hownet_dict_advanced = OpenHowNet.HowNetDict(init_sim=True)6file = open("D:\\1353776970\\æ件æ¥æ¶ï¼\\å¤åè¯è®º.csv", "r", encoding="utf-8")7line = file.readline()8positive_list = ["ç¾å³", "è¿
é", "æ¯æ", "å¤", "å¼å¿"]9negative_list = ["æ¶å¿", "ç¨å°", "è«å", "slow", "æè´µ"] # "太æ
¢", "太å°"10p_result_list = dict()11n_result_list = dict()12def deal_with_str(str_list):13 while 'ï¼' in str_list:14 str_list.remove('ï¼')15 while '\n' in str_list:16 str_list.remove('\n')17 while 'ã' in str_list:18 str_list.remove('ã')19 while 'ï¼' in str_list:20 str_list.remove('ï¼')21def p_oneword(oneword, list_p, list_n):22 if oneword not in p_result_list:23 Polarity = 024 Polarity1 = 025 Polarity2 = 026 for test_word in list_p:27 Polarity1 += hownet_dict_advanced.calculate_word_similarity(oneword, test_word)28 Polarity1 = Polarity1 / len(list_p)29 for test_word in list_n:30 Polarity2 += hownet_dict_advanced.calculate_word_similarity(oneword, test_word)31 Polarity2 = Polarity2 / len(list_n)32 Polarity = Polarity1 - Polarity233 p_result_list[oneword] = Polarity34 else:35 pass36def p_word_deal(str_list):37 while str_list:38 p_oneword(str_list.pop(0), positive_list, negative_list)39 return40def n_oneword(oneword, list_p, list_n):41 if oneword not in n_result_list:42 Polarity = 043 Polarity1 = 044 Polarity2 = 045 for test_word in list_p:46 Polarity1 += hownet_dict_advanced.calculate_word_similarity(oneword, test_word)47 Polarity1 = Polarity1 / len(list_p)48 for test_word in list_n:49 Polarity2 += hownet_dict_advanced.calculate_word_similarity(oneword, test_word)50 Polarity2 = Polarity2 / len(list_n)51 Polarity = Polarity1 - Polarity252 n_result_list[oneword] = Polarity53 else:54 pass55def n_word_deal(str_list):56 while str_list:57 n_oneword(str_list.pop(0), positive_list, negative_list)58 return59def dealwithdict(list, dict):60 for i in list:61 if i in dict:62 del dict[i]63while True:64 line = file.readline()65 if line:66 case = int(line[0])67 # seg_list = jieba.cut(line[2:], cut_all=True)68 seg_list = jieba.cut_for_search(line[2:])69 seglist = list(seg_list)70 deal_with_str(seglist)71 if case == 1:72 p_word_deal(seglist)73 else:74 n_word_deal(seglist)75 else:76 break77p_result_list_ordered = sorted(p_result_list.items(), key=lambda x: x[1], reverse=True)78n_result_list_ordered = sorted(n_result_list.items(), key=lambda x: x[1], reverse=False)79positive_file = open("positive_word.txt", "w")80negative_file = open("negative_word.txt", "w")81rp_dict = dict(p_result_list_ordered[0:60])82np_dict = dict(n_result_list_ordered[0:60])83dealwithdict(positive_list, rp_dict)84dealwithdict(negative_list, np_dict)85i = 186for key, value in rp_dict.items():87 if i > 50:88 break89 positive_file.write(str(i)+":" +key + "\n")90 i += 191i = 192for key, value in np_dict.items():93 if i > 50:94 break95 negative_file.write(str(i)+":" +key + "\n")96 i += 197file.close()98positive_file.close()...
quene_recent_requests.py
Source:quene_recent_requests.py
1'''2Calculate how many events occurred in the past 10,000 milliseconds, including the current event;3that is, for each element k in the list, calculate how many elements in the entire list are between k-10000 and k (Both ends are included).4Input format:5A sorted list mylist, all elements are non-negative integers, record the occurrence time of each request, the unit is milliseconds.6Output format:7A list of the same length as mylist.8Input sample:9[0,10,100,1000,10000,20000,100000]10Sample output:11[1,2,3,4,5,2,1]12'''13class Queue:14 def __init__(self):15 self.items = []16 def isEmpty(self):17 return self.items == []18 def enqueue(self, item):19 self.items.insert(0,item)20 def dequeue(self):21 return self.items.pop()22 def size(self):23 return len(self.items)24def func(S):25 q=Queue()26 result_list=[]27 count_previous_repeat=028 #check every element in the input list29 for s in S:30 #check if previous n items are the same, such as [0,1,1,1], mark with a counter31 if not q.isEmpty() and q.items[0]==s:32 count_previous_repeat+=133 elif not q.isEmpty() and q.items[0]!=s:34 count_previous_repeat=035 # add the testing element into the quene36 q.enqueue(s)37 # keep removing the last element in quene until the last one less than s-1000038 while s-10000> q.items[-1]:39 q.dequeue()40 # if the current element is the same sa the previous ones, then need to update the quantity of previous element in the output list41 n_result_list=len(result_list)42 ## remove previous repeated items43 while result_list!=[] and len(result_list) > n_result_list - count_previous_repeat:44 result_list.pop()45 ## update with new items46 while len(result_list) < n_result_list + 1:47 # the size of the quene is the result, which is appended at the end of the list48 result_list.append(q.size())49 return result_list50if __name__ == "__main__":51 #S = [0,0,0,1000,1000,10000,20000,100000] #test list52 S = eval(input())...
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