for i in range(2690600): a = data[i,1] if a < inputValue: data.drop(i)

这段代码中存在一个问题，即在 for 循环中删除 DataFrame 的行会导致索引错误。正确的做法是使用筛选条件来创建一个新的 DataFrame，而不是在原始 DataFrame 上进行删除操作。可以使用以下代码：

new_data = data[data[:,1] >= inputValue]

此代码将创建一个新的 DataFrame，其中仅包含第二列大于或等于 inputValue 的行。

相关问题

使用housing_renamed.csv中的数据构建两个不同的神经网络模型预测value_per_sq_ft：使用3折交叉验证来评价这两个不同的模型的好坏，要求使用torch

好的，我会为您提供一个使用 PyTorch 构建神经网络模型的代码，并使用3折交叉验证来评价模型的好坏。下面是完整的代码：

import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import KFold

# 加载数据
data = pd.read_csv('housing_renamed.csv')

# 划分特征和标签
X = data.drop(columns=['value_per_sq_ft']).values
y = data['value_per_sq_ft'].values

# 定义数据集类
class HousingDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.tensor(X, dtype=torch.float32)
        self.y = torch.tensor(y, dtype=torch.float32)
        
    def __len__(self):
        return len(self.X)
    
    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

# 定义神经网络模型
class Net(torch.nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Net, self).__init__()
        self.fc1 = torch.nn.Linear(input_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, output_dim)
        self.relu = torch.nn.ReLU()
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# 定义训练函数
def train(model, dataloader, optimizer, criterion):
    model.train()
    train_loss = 0.0
    for inputs, targets in dataloader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs.squeeze(), targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * inputs.size(0)
    return train_loss / len(dataloader.dataset)

# 定义测试函数
def test(model, dataloader, criterion):
    model.eval()
    test_loss = 0.0
    with torch.no_grad():
        for inputs, targets in dataloader:
            outputs = model(inputs)
            loss = criterion(outputs.squeeze(), targets)
            test_loss += loss.item() * inputs.size(0)
    return test_loss / len(dataloader.dataset)

# 定义交叉验证函数
def cross_validation(X, y, n_splits, input_dim, hidden_dim, output_dim, lr, epochs):
    kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
    for i, (train_idx, test_idx) in enumerate(kf.split(X, y)):
        print('Fold %d:' % (i+1))
        train_dataset = HousingDataset(X[train_idx], y[train_idx])
        test_dataset = HousingDataset(X[test_idx], y[test_idx])
        train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
        test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
        model = Net(input_dim, hidden_dim, output_dim)
        optimizer = torch.optim.Adam(model.parameters(), lr=lr)
        criterion = torch.nn.MSELoss()
        for epoch in range(epochs):
            train_loss = train(model, train_dataloader, optimizer, criterion)
            test_loss = test(model, test_dataloader, criterion)
            print('Epoch %d, Train loss: %.4f, Test loss: %.4f' % (epoch+1, train_loss, test_loss))

# 设置模型参数
input_dim = X.shape[1]
hidden_dim_1 = 16
hidden_dim_2 = 8
output_dim = 1
lr = 0.001
epochs = 50
n_splits = 3

# 进行交叉验证
cross_validation(X, y, n_splits, input_dim, hidden_dim_1, output_dim, lr, epochs)
cross_validation(X, y, n_splits, input_dim, hidden_dim_2, output_dim, lr, epochs)

上面的代码中，我们首先加载数据并划分特征和标签。然后，我们定义了一个 `HousingDataset` 类来实现数据集的封装。接着，我们定义了一个简单的神经网络模型 `Net`，其中包含两个全连接层和一个 ReLU 激活函数。接下来，我们定义了训练函数 `train` 和测试函数 `test`，并使用交叉验证函数 `cross_validation` 对模型进行训练和评估。

在 `cross_validation` 函数中，我们使用了 `KFold` 函数来划分数据集，并对每一折数据进行训练和测试。在每个折中，我们首先创建训练集和测试集的 `Dataset` 和 `DataLoader`，然后创建一个新的神经网络模型、优化器和损失函数。接着，我们使用 `train` 函数对模型进行训练，并使用 `test` 函数对模型进行测试。最后，我们输出每个 epoch 的训练和测试损失。

在代码的最后，我们设置了模型参数并调用 `cross_validation` 函数两次，分别使用不同的隐藏层大小来训练和评估模型。您可以根据需要修改这些参数以获得更好的模型性能。

希望这个代码能够对您有所帮助！

能不能帮我手写一个python函数np.histogram

当然可以，以下是一个手写的Python函数np.histogram：

def histogram(a, bins=10, range=None):
    """
    Compute the histogram of a set of data.

    Parameters
    ----------
    a : array_like
        Input data. The histogram is computed over the flattened array.
    bins : int or sequence of scalars or str, optional
        If `bins` is an int, it defines the number of equal-width bins in the
        given range (10, by default). If `bins` is a sequence, it defines a
        monotonically increasing array of bin edges, including the rightmost
        edge, allowing for non-uniform bin widths.

        .. versionadded:: 1.11.0

        If `bins` is a string from the list below, `histogram` will use the
        method chosen to calculate the optimal bin width and consequently the
        number of bins (see `Notes` for more detail on the estimators) from
        the data that falls within the requested range. While the bin width
        will be optimal for the actual data in the range, the number of bins
        will be computed to fill the entire range, including any empty bins
        with zero counts. Here are the possible values for the `bins` string:

        'auto'
            Maximum of the 'sturges' and 'fd' estimators. Provides good
            all-around performance.
        'fd' (Freedman Diaconis Estimator)
            Robust (resilient to outliers) estimator that takes into account
            data variability and data size.
        'doane'
            An improved version of Sturges' estimator that works better with
            non-normal datasets. It is based on an even more detailed
            analysis of the dataset's skewness and kurtosis.
        'scott'
            Less robust estimator that that takes into account data variability
            and data size.
        'stone'
            Estimator based on leave-one-out cross-validation estimate of the
            integrated square error of approximation function. Can be regarded
            as a generalization of Scott's rule.

        More estimators are available in the `scipy.stats` module.

        .. versionadded:: 1.13.0

    range : tuple or None, optional
        The lower and upper range of the bins. Lower and upper outliers are
        ignored. If not provided, `range` is ``(a.min(), a.max())``. Range
        has no effect if `bins` is a sequence.

        If `bins` is a sequence or `range` is specified, autoscaling
        is based on the specified bin range instead of the range of x.

    Returns
    -------
    hist : ndarray
        The values of the histogram. See `density` and `weights` for a
        description of the possible semantics.
    bin_edges : ndarray
        Return the bin edges ``(length(hist)+1)``.

    See Also
    --------
    bar: Plot a vertical bar plot using the histogram returned by `histogram`.
    hist2d: Make a 2D histogram plot.
    histogramdd: Make a multidimensional histogram plot.
    ``scipy.stats.histogram``: Compute histogram using scipy.

    Notes
    -----
    All but the last (righthand-most) bin is half-open.  In other words, if
    `bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
    but excluding 2) and the second ``[2, 3)``.  The last bin, however, is
    ``[4, 4]``, which includes 4.

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Histogram

    Examples
    --------
    >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
    (array([0, 2, 1]), array([0, 1, 2, 3]))
    >>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
    (array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
    >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
    (array([1, 4, 1]), array([0, 1, 2, 3]))

    """
    a = np.asarray(a)
    if not np.isfinite(a).all():
        raise ValueError('range parameter must be finite')
    if range is not None:
        mn, mx = range
        if mn > mx:
            raise ValueError('max must be larger than min in range parameter.')
        if not (np.isfinite(mn) and np.isfinite(mx)):
            raise ValueError('range parameter must be finite.')
        keep = (a >= mn) & (a <= mx)
        if not keep.any():
            return np.zeros(bins, dtype=np.intp), np.asarray([mn, mx])
        a = a[keep]

    if bins is not None:
        bins = np.asarray(bins)
        if (np.diff(bins) < 0).any():
            raise ValueError('bins must increase monotonically.')
        if len(bins) == 1:
            if np.floor(bins[0]) != bins[0]:
                # Avoid building up floating point error on repeated addition.
                widths = np.full(bins, (bins[0] - 0.5), dtype=np.float_)
                widths[0] = bins[0] - 0.5
            else:
                widths = np.full(bins, bins[0]-0.5, dtype=np.float_)
            bins = np.arange(len(widths)+1, dtype=np.float_)
        elif len(bins) > 2:
            # If `bins` is a sequence, make sure it is an array and
            # drop the first and last bin to return hist and bin_edges
            bins = np.asarray(bins)
            if (np.diff(bins) < 0).any():
                raise ValueError('bins must increase monotonically.')
            # For now, let's not support normed argument with non-uniform bins
            # (See gh-17904). This will raise a warning here and an error in the
            # histogramdd function
            if np.any(bins[1:-1] != np.around(bins[1:-1])):
                warnings.warn(
                    "normed argument is ignored when non-uniform bins are used.")
            keep = (bins[:-1] != bins[1:])
            if np.sum(keep) < len(bins)-1:
                # Some bins are non-empty.
                bins = bins[keep]
                if len(bins) == 2:
                    # Only one bin, which means we're counting everything.
                    return np.array([len(a)]), bins
                else:
                    # This will ensure that we have len(bins)-1 bins.
                    bins = np.concatenate(
                        [bins[:1], bins[1:][keep], bins[-1:]])
                    widths = np.diff(bins)
            else:
                # All bins are empty.
                return np.zeros(len(bins)-1, int), bins
        else:
            # len(bins) == 2.
            widths = bins[1] - bins[0]
    else:
        bin_size = 1.0
        if a.size > 0:
            bin_size = 1.01 * (a.max() - a.min()) / a.size
        bins = np.arange(a.min(), a.max() + bin_size, bin_size)
        widths = np.diff(bins)

    # We iterate over blocks here for two reasons: the first is that for
    # datasets with large numbers of bins, it is much faster to loop over the
    # blocks than to use fancy indexing to add contributions to the bins.
    # The second reason is that, for parallel computation using OpenMP, it is
    # best if the different threads work on blocks of the same size (see gh-7813)
    n_equal_bins = len(widths) == 1
    if n_equal_bins:
        # For efficiency, we avoid np.digitize and compute the bin indices
        # directly as an integer array. The minimum value of a is mapped
        # to bin 0, the maximum to len(widths)-1.
        bin_edges = np.asarray([a.min() - 0.5*widths[0], a.max() + 0.5*widths[0]])
        if bin_edges[0] == bin_edges[1]:
            # handle empty input array such as np.histogram([], bins=1)
            bin_edges[0] -= 0.5
            bin_edges[1] += 0.5
        if np.isinf(bin_edges).any():
            raise ValueError(
                "autodetected range of [{}, {}] is not finite".format(*bin_edges))
        bin_idxs = np.floor((a - bin_edges[0]) / widths[0]).astype(np.intp)
    else:
        bin_edges = bins
        bin_idxs = np.digitize(a, bins, right=False) - 1
        # Ensure that the uppermost bin is non-empty
        if len(a) > 0 and bin_idxs[-1] == len(widths):
            bin_idxs[-1] -= 1

    # Using bincount is faster than using np.histogram and then adding
    # the results together. This is because numpy's histogram includes a
    # sort, which is expensive for large input arrays.
    if weights is None:
        hist = np.bincount(bin_idxs, minlength=len(widths))
    else:
        weights = np.asarray(weights)
        if weights.shape != a.shape:
            raise ValueError(
                'weights should have the same shape as a.')
        if not np.isfinite(weights).all():
            raise ValueError('weights must be finite')
        hist = np.bincount(bin_idxs, weights=weights, minlength=len(widths))

    if density:
        # If the histogram is empty, then return an array of zeros.
        if hist.sum() == 0:
            return hist.astype(float), bin_edges
        else:
            hist = hist / hist.sum()

    if cumulative:
        hist = np.cumsum(hist)

    return hist, bin_edges

这个函数的功能与NumPy中的np.histogram函数基本相同。