def print_regresion_model_chart(self, beta_1, beta_2): self.read_source_data() lineChart = LineChart() lineChart.xdata, lineChart.ydata = (self._df["Year"].values, self._df["Value"].values) print('****************') print(lineChart.xdata, lineChart.ydata) print('****************') # Normalización self.get_normalized_data(lineChart) print('****************') print(lineChart.xdata, lineChart.ydata) print('****************') x = np.linspace(1960, 2015, 55) lineChart.x = x / max(x) lineChart.plt.figure(figsize=(8, 5)) lineChart.y = self.sigmoid(lineChart.x, *self._popt) lineChart.plt.plot(lineChart.xdata, lineChart.ydata, 'ro', label='data') lineChart.plt.plot(lineChart.x, lineChart.y, linewidth=3.0, label='fit') lineChart.plt.legend(loc='best') lineChart.set_labels('Year', 'GDP') self.calculate_model_accuracy(lineChart) return lineChart.print_to_imgb64()
def no_lineal_demo(self): self.read_source_data() lineChart = LineChart() lineChart.xdata, lineChart.ydata = (self._df["Year"].values, self._df["Value"].values) lineChart.plt.figure(figsize=(8, 5)) lineChart.plt.plot(lineChart.xdata, lineChart.ydata, 'ro') lineChart.set_labels('Year', 'GDP') return lineChart.print_to_imgb64()
def print_sigmoid_chart(self, beta_1, beta_2): self.read_source_data() lineChart = LineChart() lineChart.xdata, lineChart.ydata = (self._df["Year"].values, self._df["Value"].values) #función logística Y_pred = self.sigmoid(lineChart.xdata, beta_1, beta_2) #predicción de puntos lineChart.plt.plot(lineChart.xdata, Y_pred * 15000000000000.) lineChart.plt.plot(lineChart.xdata, lineChart.ydata, 'ro') return lineChart.print_to_imgb64()