trainDataList = list()
with open("train.csv", newline="") as csvfile:
    trainData = csv.reader(csvfile, delimiter=",",quotechar="|")
    for line in trainData:
        trainDataList.append(line[3:])

# n lines to be one day

def mod_18(n):
    return n % 18

# transpose a matrix(2D list)
def T_list(l):
    return np.array(l).T.tolist()


i = 0
trainDataIteratedPerHour = []
listToAppend = []

for item in trainDataList[1:]:
    if not mod_18(i):
        # from mod 10: 0
        listToAppend = [item]

    elif mod_18(i) == 10:
        # rain, consider "NR" = 0
        listToAppend.append(["0" if x == 'NR' else x for x in item])

    elif mod_18(i) == 17:
        # all 18 lines collected, built a set of data to extend
        listToAppend.append(item)
        trainDataIteratedPerHour.extend(T_list(listToAppend))
    else:
        # append other data
        listToAppend.append(item)
    i = i + 1

i = 0

"""
build x_data and y_data
"""

x_data = [] # 18*9 dimensions
y_data = [] # scalar, pm2.5 of next hour for x_data

for n in range(len(trainDataIteratedPerHour) - 10):
    x_data.extend(np.array(trainDataIteratedPerHour[n : n + 9]).reshape(1,162).tolist())
    y_data.append(trainDataIteratedPerHour[n + 10][9])

x_data = [[float(j) for j in i] for i in x_data]
y_data = [float(i) for i in y_data]

# draw plot of pm2.5 range?

import matplotlib
import matplotlib.pyplot as plt

x = range(len(y_data))
y = np.array(y_data)
# refer to https://matplotlib.org/2.2.2/gallery/lines_bars_and_markers/simple_plot.html

fig, ax = plt.subplots()
ax.plot(x, y)

# plt.show()

def lossFunction(w, x_data, y_data):
    w = np.array(w)
    x_data = np.array(x_data)
    result = 0.

    for i in range(len(y_data)):
        result += (y_data[i] - sum(w * x_data[i]))**2
    return result

# w = [0.01] * 162
# L = lossFunction(w,x_data,y_data)

# Iterations

def iterationRun(lr,iteration,x_data,y_data):
    # initial data
    w = [0.01] * 162
    L_history = [lossFunction(w,x_data,y_data)]

    w = np.array(w)
    x_data = np.array(x_data)

    for iterator in range(iteration):
        # initialize w_grad
        w_grad = [0.0] * 162
        # sum of all training data set
        for n in range(len(y_data)):
            # per feature
            for i in range(162):
                w_grad[i] = w_grad[i] - 2.0 * x_data[n][i] * ( sum(w * x_data[n]) - y_data[n] )

        # update w
        for i in range(162):
            w[i] = w[i] + lr * w_grad[i]

        # store Loss Function hisotry for plotting
        L_history.append(lossFunction(w,x_data,y_data))
        print (str(iterator) + " : " + str(datetime.datetime.now().time()) + " L:" + str(L_history[-1]))
    return L_history

lr = 0.0000000000001 # learning rate
iteration = 10
iterationRun(lr,iteration,x_data,y_data)

In [19]: lr = 0.0000000000001 # learning rate
    ...: iteration = 10
    ...: iterationRun(lr,iteration,x_data,y_data)
    ...:
0 : 13:21:22.227145 L:5897161.869645979
1 : 13:21:53.988814 L:5890818.193569232
2 : 13:22:25.639150 L:5884483.073933817
3 : 13:22:57.211950 L:5878156.49918669
4 : 13:23:28.873442 L:5871838.457790465
5 : 13:24:01.059230 L:5865528.93822318