빅데이터 이해 및 활용 20190401

def vector_add(v, w):
    return [v_i+w_i for v_i, w_i in zip(v,w)]

def vector_sum(vectors):
    result = vectors[0]
    for vector in vectors[1:]:
        result = vector_add(result, vector)
    return result

vectors=[[170,65,30],[180,74,28],[168,60,20]]

vector_sum(vectors)

vector_add(vectors[0],vectors[1])



def scalar_multiply(c,v):
    return [c*v_i for v_i in v]

def vector_mean(vectors):
    n = len(vectors)
    return scalar_multiply(1/n, vector_sum(vectors))

vector_mean(vectors)


<벡터와 내적>

v=[1,2,3]
w=[4,5,6]
def dot(v, w):
    return sum(v_i*w_i for v_i, w_i in zip(v,w))

dot(v,w)

 

<각 벡터 성분의 제곱의 합>

 

v=[1,2,3]
w=[4,5,6]
def dot(v, w):
    return sum(v_i*w_i for v_i, w_i in zip(v,w))

dot(v,w)

dot(v,v) #각 제곱의  합 

 

v=[1,2,3]
w=[4,5,6]
def dot(v, w):
    return sum(v_i*w_i for v_i, w_i in zip(v,w))

def vector_subtract(v,w):
    return [v_i-w_i for v_i, w_i in zip(v,w)]

def squared_distance(v,w):
    v_minus_w = vector_subtract(v,w)
    return dot(v_minus_w, v_minus_w)


def distance(v,w):
    return pow(squared_distance(v,w),1/2)

distance(v,w)

 

v=[1,2,3]
w=[4,5,6]
def dot(v, w):
    return sum(v_i*w_i for v_i, w_i in zip(v,w))

def vector_subtract(v,w):
    return [v_i-w_i for v_i, w_i in zip(v,w)]

def squared_distance(v,w):
    v_minus_w = vector_subtract(v,w)
    return dot(v_minus_w, v_minus_w)


def distance(v,w):
    return pow(squared_distance(v,w),1/2)

distance(v,w)

A=[[1,2,3],
   [4,5,6]]

def shape(A):
    return len(A), len(A[0])

shape(A)

B=[[1,2],
   [3,4],
   [5,6],
   [7,8]]

shape(B)

def get_row(A, i):
    return A[i]

get_row(A,0)

get_row(A,1)

def get_col(A,j):
    return [A_i[j] for A_i in A]

get_col(A,2)