fit（）缺少1个必需的位置参数：“ theta”

Question

我试图实现逻辑回归模型。 我收到以下错误消息

  

TypeError：fit（）缺少1个必需的位置参数：“ theta”

这是我的代码

if __name__ == "__main__":
    # X = feature values, all the columns except the last column
    X = data.iloc[:, :-1]

    # y = target values, last column of the data frame
    y = data.iloc[:, -1]

    # filter out the applicants that got admitted
    admitted = data.loc[y == 1]

    # filter out the applicants that din't get admission
    not_admitted = data.loc[y == 0]

X = np.c_[np.ones((X.shape[0], 1)), X]
y = y[:, np.newaxis]
theta = np.zeros((X.shape[1], 1))

def sigmoid(x):
    # Activation function used to map any real value between 0 and 1
    return 1 / (1 + np.exp(-x))

def net_input(theta, x):
    # Computes the weighted sum of inputs
    return np.dot(x, theta)

def probability(theta, x):
    # Returns the probability after passing through sigmoid
    return sigmoid(net_input(theta, x))

def cost_function(self, theta, x, y):
    # Computes the cost function for all the training samples
    m = x.shape[0]
    total_cost = -(1 / m) * np.sum(
        y * np.log(probability(theta, x)) + (1 - y) * np.log(
            1 - probability(theta, x)))
    return total_cost

def gradient(self, theta, x, y):
    # Computes the gradient of the cost function at the point theta
    m = x.shape[0]
    return (1 / m) * np.dot(x.T, sigmoid(net_input(theta,   x)) - y)

def fit(self, x, y, theta):
    opt_weights = fmin_tnc(func=cost_function, x0=theta,
                  fprime=gradient,args=(x, y.flatten()))
    return opt_weights[0]
parameters = fit(X, y, theta)

  

TypeError：fit（）缺少1个必需的位置参数：“ theta”