title: Q2: AttributeError: 'builtin_function_or_method' object has no attribute 'size'
body: <blockquote>
  <pre><code>Could anyone tell me why I'm getting the error type: AttributeError: 'builtin_function_or_method' object has no attribute
  </code></pre>
    
    <p>'size' in like 57?<br>
        for this synthax: <code>out=np.zeros((x.size,y.size))</code></p>
  </blockquote>
  
  <pre><code>import numpy as np
  import sympy as sp
  from numpy import exp,sqrt,pi
  from sympy import Integral, log, exp, sqrt, pi
  import math
  from numpy import array
  import matplotlib.pyplot as plt 
  import scipy.integrate
  from scipy.special import erf
  from scipy.stats import norm, gaussian_kde
  from quantecon import LAE
  from sympy.abc import q
  #from sympy import symbols
  #var('q')
  #q= symbols('q')
  
  ## == Define parameters == #
  mu=80
  sigma=20
  b=0.2
  Q=80
  Q1=Q*(1-b)
  Q2=Q*(1+b)
  d = (sigma*np.sqrt(2*np.pi))
  phi = norm()
  n = 500
  
  #Phi(z) = 1/2[1 + erf(z/sqrt(2))].
  
  def p_k_positive(x, y):
     # x, y = np.array(x, dtype=float), np.array(y, dtype=float)
      Positive_RG = norm.pdf(x[:, None] - y[None, :]+Q1, mu, sigma)
      print('Positive_R = ', Positive_RG)
      return Positive_RG 
  
  def p_k_negative(x, y):
     # x, y = np.array(x, dtype=float), np.array(y, dtype=float)
      Negative_RG = norm.pdf(x[:, None] - y[None, :]+Q2, mu, sigma) 
      print('Negative_RG = ', Negative_RG)
      return Negative_RG 
  
  def p_k_zero(x, y):
     # x, y = np.array(x, dtype=float), np.array(y, dtype=float)
      Zero_RG = (1/(2*math.sqrt(2*math.pi)))*(erf((x[:, None]+Q2-mu)/(sigma*math.sqrt(2)))-erf((x[:, None]+Q1-mu)/(sigma*math.sqrt(2))))
      #Zero_RG =norm.pdf
      print('Zero_RG',Zero_RG)
      return Zero_RG
  
  def myFilter(x,y):
      x, y = x.squeeze, y.squeeze
      out=np.zeros((x.size,y.size))
      xyDiff = x[:, None] - y[None, :]
      out=np.where(np.bitwise_and(y[None, :] &gt; 0.0, xyDiff &gt;= -Q1), p_k_positive(x, y), out) # unless the sum functions are different
      out=np.where(np.bitwise_and(y[None, :] &lt; 0.0, x[:, None] &gt;= -Q1), p_k_negative(x, y), out)
      out=np.where(np.bitwise_and(y[None, :] ==0.0, xyDiff &gt;= -Q1), p_k_zero(x, y), out)
      return out
  
  
  Z = phi.rvs(n)
  X = np.empty(n)
  for t in range(n-1):
      X[t+1] = X[t] + Z[t]
      #X[t+1] = np.abs(X[t]) + Z[t]
  psi_est = LAE(myFilter, X)
  k_est = gaussian_kde(X)
  
  fig, ax = plt.subplots(figsize=(10,7))
  ys = np.linspace(-200.0, 200.0, 200)
  ax.plot(ys, psi_est(ys), 'g-', lw=2, alpha=0.6, label='look ahead estimate')
  ax.plot(ys, k_est(ys), 'k-', lw=2, alpha=0.6, label='kernel based estimate')
  ax.legend(loc='upper left')
  plt.show()
  </code></pre>