手書き数字をpythonでもてあそぶ その２（識別する） ref: http://qiita.com/kenmatsu4/items/2d21466078917c200033

file0.txt

C = \{0, 1, 2, 3, 4, 5, 6, 7, 8, 9\}

file1.txt

y_i= (y_1, y_2,...,y_{784}) 　　　　　 (i=0,1,...,9)

file2.txt

\hat{y}_i = \frac{1}{n_i}\sum_{j=1}^{n_i} y_j

file3.txt

x_j= (x_1, x_2,...,x_{784})

file4.txt

{\rm argmin}_i{({\rm distance}_i)} = {\rm argmin}_i{(\|\hat{y}_i - x_j\|)}

file5.py

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from collections import defaultdict

file6.py

image_dict = dict()

def add_image(label, image_vector):
    vec = np.array(image_vector)
    if label in image_dict:
        image_dict[label] += vec
    else:
        image_dict[label] = vec
    return image_dict

label_dd = defaultdict(int)
def count_label(data):
    for d in data:
        label_dd[d[0]] += 1
    return label_dd

def plot_digits(X, Y, Z, size_x, size_y, counter, title):
    plt.subplot(size_x, size_y, counter)
    plt.title(title)
    plt.xlim(0,27)
    plt.ylim(0,27)
    plt.pcolor(X, Y, Z)
    plt.gray()
    plt.tick_params(labelbottom="off")
    plt.tick_params(labelleft="off")

file7.py

size = 28
raw_data= np.loadtxt('train_master.csv',delimiter=',',skiprows=1)

# draw digit images
plt.figure(figsize=(11, 6))

# data aggregation
for i in range(len(raw_data)):
    add_image(raw_data[i,0],raw_data[i,1:785])

count_dict = count_label(raw_data)

standardized_digit_dict = dict()   # 代表値を格納する辞書オブジェクト
count = 0
for key in image_dict.keys(): 
    count += 1
    X, Y = np.meshgrid(range(size),range(size))
    num = label_dd[key]
    Z = image_dict[key].reshape(size,size)/num
    Z = Z[::-1,:]
    standardized_digit_dict[int(key)] = Z
    plot_digits(X, Y, standardized_digit_dict[int(key)], 2, 5, count, "")

plt.show()

file8.py

test_data= np.loadtxt('test_small.csv',delimiter=',',skiprows=1)

# compare 1 tested digit vs average digits with norm 
plt.figure(figsize=(10, 9))

for i in range(1):    # 最初の１つだけ試してみる
    result_dict = defaultdict(float)

    X, Y = np.meshgrid(range(size),range(size))
    Z = test_data[i].reshape(size,size)
    Z = Z[::-1,:]
    flat_Z = Z.flatten()
    plot_digits(X, Y, Z, 3, 4, 1, "tested")

    count = 0
    for key in standardized_digit_dict.keys(): 
        count += 1
        X1 = standardized_digit_dict[key]
        flat_X1 = standardized_digit_dict[key].flatten()
        norm = np.linalg.norm(flat_X1 - flat_Z)   # 各代表値と識別対象データとの距離の導出
        plot_digits(X, Y, X1, 3, 4, (1+count), "d=%.3f"% norm)

plt.show()

file9.py

# recognize digits
plt.figure(figsize=(15, 130))

for i in range(len(test_data)):
    result_dict = defaultdict(float)

    X, Y = np.meshgrid(range(size),range(size))
    tested = test_data[i].reshape(size,size)
    tested = tested[::-1,:]
    flat_tested = tested.flatten()

    norm_list=[]
    count = 0
    for key in standardized_digit_dict.keys(): 
        count += 1
        sdd = standardized_digit_dict[key]
        flat_sdd = sdd.flatten()
        norm = np.linalg.norm(flat_sdd - flat_tested)
        norm_list.append((key, norm))

    norm_list = np.array(norm_list)
    min_result = norm_list[np.argmin(norm_list[:,1])]

    plot_digits(X, Y, tested, 40, 5, i+1, "l=%d, n=%d" % (min_result[0], min_result[1]))

plt.show()