y-mitsui

import numpy as np
from scipy import stats
import math

class ModelSelection:
    """
    Distribution selector using AIC
    """
    
    def estNormal(self, sample):

y-mitsui

/*
 cuSolverを使ってコレスキー分解の逆行列を計算。
 AX=I(Iは単位行列)として逆行列Xを求める
 
 gcc -o cusolver_inverse_matrix -I/usr/local/cuda/include/ -L/usr/local/cuda/lib64/ cusolver_inverse_matrix.c -lcudart -lcusolver

*/

#include <cuda_runtime.h>
#include <cusolverDn.h>

y-mitsui

import numpy.random.mtrand
import numpy as np

class LdaCvb0:
    def __init__(self, word_indexes, word_counts, n_topics, alpha=0.1, beta=0.01):
        self.word_counts = word_counts
        self.word_indexes = word_indexes
        self.n_words = np.max(np.max(word_indexes)) + 1
        self.n_topics = n_topics

y-mitsui

#! /usr/bin/env python
# -*- coding:utf-8 -*-
"""
多重コレスポンデンス分析ライブラリ(https://github.com/esafak/mca)の使用例
"""
import numpy as np
import numpy.random as random
import matplotlib.pyplot as plt
import pandas as pd
import mca

y-mitsui

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
matplotlibで都道府県地図を描画
"""
import matplotlib.path as mpath
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Wedge, Polygon
from matplotlib.collections import PatchCollection

y-mitsui

#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
自然な共益事前分布を使って線形回帰モデルの係数をベイズ推定
【事前分布】
 平均：多変量正規分布
"""
from numpy.random import *
import numpy as np
from mpl_toolkits.mplot3d import Axes3D

y-mitsui

#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
自然な共益事前分布を使って正規分布のパラメーターをベイズ推定
【事前分布】
 平均：正規分布
 分散:逆ガンマ分布
"""
import numpy as np
import scipy.stats

y-mitsui

#! /usr/bin/env python
# -*- coding: utf-8 -*-
import xgboost as xgb
import numpy as np
import scipy.stats
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import math
import sys
from hyperopt import hp