横井_backup の履歴(No.66) | 奥原研究室・Rene研究室ログ

https://www.it-swarm-ja.tech/ja/javascript/base64%E3%81%A7%E3%82%A8%E3%83%B3%E3%82%B3%E3%83%BC%E3%83%89%E3%81%95%E3%82%8C%E3%81%9F%E3%82%A4%E3%83%A1%E3%83%BC%E3%82%B8%E3%82%92%E3%83%80%E3%82%A6%E3%83%B3%E3%83%AD%E3%83%BC%E3%83%89%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/1070167969/
#!/home/zeta/.pyenv/shims/python
# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np
import sys
import json
import cgi
import os
from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
import matplotlib
import base64
import datetime

print("Content-type: application/json")
print("

data = sys.stdin.read()
df = pd.read_json(data)
#df = pd.DataFrame(np.random.rand(16*21).reshape(21,16))
#df = pd.read_csv('blocklyinputandoutputtwo.csv',header=None)
df_two = df.T
df_label = df_two[2].tolist()
#print(df_label)
#df_tenti = df_two.set_index(2)
#df_tenti = df_tenti.drop(df_tenti.columns[[0,1,2]], axis=1)
#print(df_tenti)
df_tenti = df_two.drop(df_two.columns[[0,1,2,3]], axis=1)
#print(df_tenti)
Z = linkage(df_tenti,method="ward",metric="euclidean")

#print(Z)

today = datetime.datetime.now()

dendrogram(Z, labels=df_label)
path = "/usr/lib/cgi-bin/" + str(today) + ".png"
matplotlib.pyplot.savefig(path)
with open(path, 'br') as f1:
    b64_img = base64.b64encode(f1.read())
    #print(str(b64_img))
os.remove(path)
#wine_json = json.dumps(df.values.tolist())
#print(wine_json)
print(json.JSONEncoder().encode(b64_img.decode()))
#asasaa={"a":str(b64_img)}
#print(str(b64_img))
#print(json.JSONEncoder().encode(asasaa))
#result = {"回帰係数":1,"切片":2,"決定係数":3}

#print(json.JSONEncoder().encode(result))
print('
')

#!/home/zeta/.pyenv/shims/python

# -*- coding: utf-8 -*-

from sklearn.datasets import load_boston from sklearn.neural_network import MLPRegressor import sys import json import cgi import matplotlib.pyplot as plt import numpy as np import random import pandas as pd

print("Content-type: application/json") print("\n\n")

data = sys.stdin.read()

#np.set_printoptions(threshold=np.inf)

#df = pd.read_csv('blocklyinputandoutputtwo.csv',header=None) df = pd.read_json(data) new_df=df[4:]

#print(df)

# データセットを読み込む

#boston = load_boston()

#print(boston)

#x = boston.data x = df.iloc[4:][7].tolist() x = [float(s) for s in x] x = [x] x = np.array(x, dtype=object).T

#print(x)

#y = boston.target y = df.iloc[4:][10].tolist() y = [float(s) for s in y] y = np.array(y, dtype=object)

#print(y)

# 読み込んだデータセットをシャッフルする

#p = list(zip(x, y))

#random.shuffle(p)

#x, y = zip(*p)

# 学習データの件数を指定する train_size = 300 test_size = len(x) - train_size

# データセットを学習データとテストデータに分割する train_x = x[:train_size] train_y = y[:train_size] test_x = x test_y = y

# ニューラルネットワークの学習を行う mlp = MLPRegressor(hidden_layer_sizes=(100,), max_iter=500) mlp.fit(train_x, train_y)

#a = mlp.score(test_x,test_y)

# 学習させたモデルを使ってテストデータに対する予測を出力する pred = mlp.predict(test_x)

# グラフを描画する際の値の範囲を設定する values = np.concatenate([test_y, pred], 0) ptp = np.ptp(values) min_value = np.min(values) - ptp * 0.1 max_value = np.max(values) + ptp * 0.1

predict_list = ["", "", "予測値", "float"] predict_list.extend(pred) df[new_df.iloc[0, :].size]=pd.DataFrame(predict_list)

#print(pred)

#print(type(pred))

#result = {"スコア":a} wine_json = json.dumps(df.values.tolist()) print(wine_json)

#print(df[:2].to_json(orient='split'))

#print('\n')

#print(df[:2].to_json(orient='records'))

#print('\n')

#print(df[:2].to_json(orient='index'))

#print('\n')

#print(df[:2].to_json(orient='columns'))

#print('\n')