WIP: CLI interface interact with Model class, subprogram call Model methods to satisfy the subject

1 files changed, 80 insertions, 0 deletions

diff --git a/src/model.py b/src/model.py
new file mode 100644
index 0000000..8fb34a9
--- /dev/null
+++ b/src/model.py
@@ -0,0 +1,80 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import normalize as sklearn_normalize
+
+
+class Model:
+    def __init__(self, datafilename="../data.csv", thetafilename="./theta"):
+        self.datafilename = datafilename
+        self.thetafilename = thetafilename
+        self.theta1, self.theta0 = self._read_theta()
+        self.xs, self.ys = self._read_data()
+
+    def train(self, alpha=1, epoch=100):
+        self.xs, self.ys = self._normalize_data()
+        for _ in range(epoch):
+            next_theta0 = self.theta0 - alpha * self._partial_theta0()
+            next_theta1 = self.theta1 - alpha * self._partial_theta1()
+            self.theta0 = next_theta0
+            self.theta1 = next_theta1
+
+    def write_theta(self):
+        with open(self.datafilename, "w") as file:
+            file.write("{},{}".format(str(theta1), str(theta0)))
+
+    def hypothesis(self, x):
+        return x * self.theta1 + self.theta0
+
+    def cost(self):
+        return (1 / (2 * len(self.xs))) * sum([(self.hypothesis(x) - y) ** 2
+                                               for x, y in zip(self.xs, self.ys)])
+
+    def plot(self, plot_data=True, plot_model=True):
+        self.fig, self.ax = plt.subplots()
+        if plot_data:
+            self._plot_data()
+        if plot_model:
+            self._plot_model()
+        plt.show()
+
+    def _plot_data(self):
+        self.ax.scatter(self.xs, self.ys)
+
+    def _plot_model(self):
+        line_xs = [self.xs.min(), self.xs.max()]
+        line_ys = [self.hypothesis(x) for x in line_xs]
+        self.ax.plot(line_xs, line_ys)
+
+    def _partial_theta1(self):
+        return sum([(self.hypothesis(x) - y) * x
+                    for x, y in zip(self.xs, self.ys)]) / len(self.xs)
+
+    def _partial_theta0(self):
+        return sum([self.hypothesis(x) - y
+                    for x, y in zip(self.xs, self.ys)]) / len(self.xs)
+
+    def _normalize_data(self):
+        self.xs, self.ys = sklearn_normalize([self.xs, self.ys])
+
+    def _read_theta(self):
+        try:
+            with open(self.thetafilename, "r") as file:
+                strs = file.read().strip().split(",")
+                if len(strs) != 2:
+                    raise "wrong theta file format"
+                return int(strs[0]), int(strs[1])
+        except IOError:
+            print(self.thetafilename, "do not exist")
+
+    def _read_data(self):
+        try:
+            data = np.genfromtxt(self.datafilename, delimiter=",")
+            return data[:, 0], data[:, 1]
+        except IOError:
+            print(self.datafilename, "do not exist")
+
+
+if __name__ == "__main__":
+    m = Model()
+    m.train()
+    m.write_theta()