#ISPN医学英语每日十词[超话]##ISPN国际护士证##护士[超话]##美国注册护士RN#
[加油]DAY216 女性生殖系统词汇(三)
①lactiferous ducts /læk'tɪfərəs/ /dʌkts/ 输乳管
②menstrual cycle 月经周期
③estrogen /'ɛstrədʒən/雌性激素
④progesterone /pro'dʒɛstə'ron/ 黄体酮
⑤menstrual phase月经期
⑥postmenstrual 月经后期
⑦proliferative phase/prə,lifə'reitiv/ /fez/ 增生期
⑧ovulatory phase /'əuvjulətəri//fez/ 排卵期
⑨premenstrual /,pri'mɛnstruəl/经前的,月经前的
⑩secretory phase /sɪ'kritəri/ /fez/分泌期
[加油]DAY216 女性生殖系统词汇(三)
①lactiferous ducts /læk'tɪfərəs/ /dʌkts/ 输乳管
②menstrual cycle 月经周期
③estrogen /'ɛstrədʒən/雌性激素
④progesterone /pro'dʒɛstə'ron/ 黄体酮
⑤menstrual phase月经期
⑥postmenstrual 月经后期
⑦proliferative phase/prə,lifə'reitiv/ /fez/ 增生期
⑧ovulatory phase /'əuvjulətəri//fez/ 排卵期
⑨premenstrual /,pri'mɛnstruəl/经前的,月经前的
⑩secretory phase /sɪ'kritəri/ /fez/分泌期
#数据科学家#我的第一条微博#
打开文件,把数据转换成scikit learn 支持的格式。
主要是把,X 数据 和y 类别信息从文件中提取出来, 存在相应的变量里。
用jupyter notebook.
import csv
import numpy as np
data_folder = "C:\\documents\\bioinformatic\\Data"
file_to_open = data_folder + "\\Dataset1.csv"
print(file_to_open)
with open(file_to_open,'r') as train_file:
lines = list(csv.reader(train_file))
feature_num = len(lines)-1
array_row = np.array(lines[1])
sample_num = len(array_row)-1
#sample_num = len(lines[0].split(','))
print("array_row[0] "+ str(array_row[0]))
print("feature_num " + str(feature_num))
print("sample_num " + str(sample_num))
print("array_row[1] " + str(array_row[1]))
#data = np.arange(sample_num*feature_num,dtype= np.float64 ).reshape(sample_num,feature_num)
data = np.zeros((sample_num,feature_num))
feature_names=["" for x in range(feature_num)]
for i in range(0,feature_num):
# get a gene line
array_string_row = lines[i+1]
array_np_row = np.array(lines[i+1])
# get the gene name
feature_names[i]=str(array_string_row[0])
for j in range(0,sample_num):
# get each data of the sample
data[j][i] = float(array_np_row[j+1])
print("The first sample data is :"+ str(data[0]))
print(" The first gene name feature is :" + str(feature_names[0]))
print("The second sample data is :"+ str(data[2]))
print(" The second gene name feature is :" + str(feature_names[1]))
train_file.close()
打开文件,把数据转换成scikit learn 支持的格式。
主要是把,X 数据 和y 类别信息从文件中提取出来, 存在相应的变量里。
用jupyter notebook.
import csv
import numpy as np
data_folder = "C:\\documents\\bioinformatic\\Data"
file_to_open = data_folder + "\\Dataset1.csv"
print(file_to_open)
with open(file_to_open,'r') as train_file:
lines = list(csv.reader(train_file))
feature_num = len(lines)-1
array_row = np.array(lines[1])
sample_num = len(array_row)-1
#sample_num = len(lines[0].split(','))
print("array_row[0] "+ str(array_row[0]))
print("feature_num " + str(feature_num))
print("sample_num " + str(sample_num))
print("array_row[1] " + str(array_row[1]))
#data = np.arange(sample_num*feature_num,dtype= np.float64 ).reshape(sample_num,feature_num)
data = np.zeros((sample_num,feature_num))
feature_names=["" for x in range(feature_num)]
for i in range(0,feature_num):
# get a gene line
array_string_row = lines[i+1]
array_np_row = np.array(lines[i+1])
# get the gene name
feature_names[i]=str(array_string_row[0])
for j in range(0,sample_num):
# get each data of the sample
data[j][i] = float(array_np_row[j+1])
print("The first sample data is :"+ str(data[0]))
print(" The first gene name feature is :" + str(feature_names[0]))
print("The second sample data is :"+ str(data[2]))
print(" The second gene name feature is :" + str(feature_names[1]))
train_file.close()
#英雄联盟# #2019LPL# 【外国网友看LPL春决次局】
Str1kon:人们想要看到imp重复他在2015年的冠军级表现
LakersLAQ:虽然JDG进入了决赛,但他们确实不是LPL第二好的队伍,大多数打进MSI的战队都比JDG好,不幸的是FPX和TOP
the_next core:G2意外地击败了RNG,JDG意外地击败了FPX,然后他们遇到了iG
Is_J_a_Name:难道这场决赛会比iG和FNC的决赛更快结束?
Marky0choa:希望这个系列赛能更久些,如果结果是 3:0 我就把鞋吃了
Str1kon:人们想要看到imp重复他在2015年的冠军级表现
LakersLAQ:虽然JDG进入了决赛,但他们确实不是LPL第二好的队伍,大多数打进MSI的战队都比JDG好,不幸的是FPX和TOP
the_next core:G2意外地击败了RNG,JDG意外地击败了FPX,然后他们遇到了iG
Is_J_a_Name:难道这场决赛会比iG和FNC的决赛更快结束?
Marky0choa:希望这个系列赛能更久些,如果结果是 3:0 我就把鞋吃了
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