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@EnsekiTT
EnsekiTT / blender_init.py
Last active August 29, 2015 14:26
Blenderで全てスクリプトから生成するときに毎回オブジェクトが蓄積しないようにする
#!BPY
import bpy
def clear_all():
for item in bpy.context.scene.objects:
bpy.context.scene.objects.unlink(item)
for item in bpy.data.objects:
bpy.data.objects.remove(item)
for item in bpy.data.meshes:
bpy.data.meshes.remove(item)
@EnsekiTT
EnsekiTT / TensorFlow_MNIST_without_input_data_py.py
Created November 14, 2015 17:28
TensorFlowのMNISTチュートリアルをinput_data.pyを使わずにやってみる。
# coding: utf-8
# In[26]:
get_ipython().magic(u'matplotlib inline')
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
@EnsekiTT
EnsekiTT / Chainer_MNIST_without_data.py
Last active January 11, 2016 15:21
Chainer_MNIST_without_datainput.py
#!/usr/bin/env python
import numpy as np
import pandas as pd
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import optimizers
from chainer import serializers
@EnsekiTT
EnsekiTT / rename.rb
Created February 24, 2016 15:07
rename.rbを該当ディレクトリで実行すればなんとなく一斉に変更できる
dirlist = Dir::entries(".")
# ファイルをフィルタリングする
targets = dirlist.grep(/(.+)\.wav/)
# 変更ルールを決める
renametable = Array(targets.length)
targets.each_with_index do |name, i|
print i%19+1, ","
print i/19+1, "\n"
@EnsekiTT
EnsekiTT / rl_gambler.py
Last active February 27, 2016 02:12
Reinforcement Learningの4章にあるプログラミングをやってみた。ギャンブラーの最適行動について。
# -*- coding:utf-8 -*-
import matplotlib.pyplot as plt
## 定数達
# ギャンブラーの所持金
S = list(range(1, 100))
# 状態価値関数
V = 101*[0.0]
# 勝利条件
V[100] = 1.0
@EnsekiTT
EnsekiTT / WhatMnist.py
Last active March 30, 2016 12:21
DeepLearningだ!と意気込んだものの手書き数字認識の後に続かなくなった時に読むデータそのものの話 ref: http://qiita.com/EnsekiTT@github/items/66ae1b00a0fefbd036d0
n = 10
x = Variable(x_test[n:n+1])
v = model.predictor(x)
plt.imshow(x_test[n:n+1].reshape((28,28)), cmap = cm.Greys_r)
print(np.argmax(v.data))
@EnsekiTT
EnsekiTT / quaternion.py
Created May 31, 2016 18:01
az->el->roll で回すクォータニオンの確認
# -*- coding: utf-8 -*-
import numpy as np
from sympy import *
# az el roll [rad]
degang = [0,45,0]
[az, el, roll] = np.array(degang)*np.pi/180
q_az = np.array([0,0,np.sin(az/2),np.cos(az/2)])
q_el = np.array([0,np.sin(el/2),0,np.cos(el/2)])
@EnsekiTT
EnsekiTT / ant.py
Last active October 29, 2016 16:47
サラリーマンが、セールスマンと蟻で遊んだ
# coding utf-8
import copy
import random
from math import *
import matplotlib.pyplot as plt
class Agent():
def __init__(self, towns, roads, start, pheromone):
# value
@EnsekiTT
EnsekiTT / extensions_test.py
Created November 23, 2016 17:26
chainer.trainingのextensions全部試す
import numpy as np
import chainer
from chainer import Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions
class MyModel(Chain):
require 'torch'
torch.manualSeed(114514)
-- choose a dimension
N = 5
-- create a random NxN matrix
A = torch.rand(N, N)