import numpy as np
import matplotlib.pyplot as plt

data = np.load("./basis_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy")
plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k", label="basis nnmf")

data = np.load("./basis_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy")
plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k--", label="basis mlp")

data = np.load(
    "./basis_nnmf_autograd/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(
    data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k:", label="basis nnmf autograd"
)

data = np.load("./basis_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy")

plt.loglog(
    data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k-.", label="basis conv2d"
)


# ----

data = np.load(
    "./max_pooling_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="nnmf max pooling")

data = np.load(
    "./avg_pooling_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(
    data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="nnmf average pooling"
)

data = np.load(
    "./avg_pooling_nnmf_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(
    data[:, 0],
    100.0 * (1.0 - data[:, 1] / 10000.0),
    label="nnmf average noinbetween1x1",
)
# ----
data = np.load(
    "./avg_pooling_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(
    data[:, 0],
    100.0 * (1.0 - data[:, 1] / 10000.0),
    label="conv2d average pooling (breaks during learning)",
)

data = np.load(
    "./avg_pooling_conv2d_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(
    data[:, 0],
    100.0 * (1.0 - data[:, 1] / 10000.0),
    label="conv2d average noinbetween1x1",
)

# ----

data = np.load(
    "./max_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="mlp max pooling")

data = np.load(
    "./avg_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(
    data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="mlp average pooling"
)

data = np.load(
    "./avg_pooling_mlp_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy"
)
plt.loglog(
    data[:, 0],
    100.0 * (1.0 - data[:, 1] / 10000.0),
    label="mlp average noinbetween1x1",
)

plt.legend()
plt.xlabel("Epoch")
plt.ylabel("Error [%]")
plt.title("CIFAR10")
plt.show()